[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-spmallick--learnopencv":3,"tool-spmallick--learnopencv":62},[4,18,26,36,46,54],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",159636,2,"2026-04-17T23:33:34",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":42,"last_commit_at":43,"category_tags":44,"status":17},8272,"opencode","anomalyco\u002Fopencode","OpenCode 是一款开源的 AI 编程助手（Coding Agent），旨在像一位智能搭档一样融入您的开发流程。它不仅仅是一个代码补全插件，而是一个能够理解项目上下文、自主规划任务并执行复杂编码操作的智能体。无论是生成全新功能、重构现有代码，还是排查难以定位的 Bug，OpenCode 都能通过自然语言交互高效完成，显著减少开发者在重复性劳动和上下文切换上的时间消耗。\n\n这款工具专为软件开发者、工程师及技术研究人员设计，特别适合希望利用大模型能力来提升编码效率、加速原型开发或处理遗留代码维护的专业人群。其核心亮点在于完全开源的架构，这意味着用户可以审查代码逻辑、自定义行为策略，甚至私有化部署以保障数据安全，彻底打破了传统闭源 AI 助手的“黑盒”限制。\n\n在技术体验上，OpenCode 提供了灵活的终端界面（Terminal UI）和正在测试中的桌面应用程序，支持 macOS、Windows 及 Linux 全平台。它兼容多种包管理工具，安装便捷，并能无缝集成到现有的开发环境中。无论您是追求极致控制权的资深极客，还是渴望提升产出的独立开发者，OpenCode 都提供了一个透明、可信",144296,1,"2026-04-16T14:50:03",[13,45],"插件",{"id":47,"name":48,"github_repo":49,"description_zh":50,"stars":51,"difficulty_score":32,"last_commit_at":52,"category_tags":53,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108322,"2026-04-10T11:39:34",[14,15,13],{"id":55,"name":56,"github_repo":57,"description_zh":58,"stars":59,"difficulty_score":32,"last_commit_at":60,"category_tags":61,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[45,13,15,14],{"id":63,"github_repo":64,"name":65,"description_en":66,"description_zh":67,"ai_summary_zh":68,"readme_en":69,"readme_zh":70,"quickstart_zh":71,"use_case_zh":72,"hero_image_url":73,"owner_login":74,"owner_name":75,"owner_avatar_url":76,"owner_bio":77,"owner_company":78,"owner_location":79,"owner_email":78,"owner_twitter":78,"owner_website":80,"owner_url":81,"languages":82,"stars":120,"forks":121,"last_commit_at":122,"license":78,"difficulty_score":10,"env_os":123,"env_gpu":124,"env_ram":125,"env_deps":126,"category_tags":137,"github_topics":138,"view_count":32,"oss_zip_url":78,"oss_zip_packed_at":78,"status":17,"created_at":151,"updated_at":152,"faqs":153,"releases":184},8931,"spmallick\u002Flearnopencv","learnopencv","Learn OpenCV  : C++ and Python Examples","learnopencv 是一个专注于计算机视觉、深度学习与人工智能领域的开源代码库，旨在将复杂的技术理论转化为可运行的 C++ 和 Python 实战示例。它紧密配合 LearnOpenCV.com 博客的技术文章，为读者提供从基础概念到前沿应用的完整代码实现。\n\n面对 AI 技术迭代快、论文复现难的问题，learnopencv 提供了经过验证的落地方案，帮助用户跨越从“读懂原理”到“写出代码”的鸿沟。无论是实时目标检测（如最新的 YOLO26、RF-DETR）、多目标跟踪、人脸隐私保护，还是大模型部署（如 Jetson 边缘计算、vLLM 服务）、3D 重建（SAM 3D、高斯泼溅）以及 RAG 检索增强生成等热门方向，这里都能找到对应的演示项目。\n\n该资源特别适合开发者、算法研究人员及 AI 学习者使用。对于希望提升工程能力的程序员，它提供了生产级的参考架构；对于科研人员，它是快速验证新想法的试验田；对于初学者，则是循序渐进掌握 OpenCV 与深度学习框架的最佳实践指南。通过涵盖从传统图像处理到大模型应用的全栈内容，learnopencv 致力于让每个人都能轻松上手并精通 AI ","learnopencv 是一个专注于计算机视觉、深度学习与人工智能领域的开源代码库，旨在将复杂的技术理论转化为可运行的 C++ 和 Python 实战示例。它紧密配合 LearnOpenCV.com 博客的技术文章，为读者提供从基础概念到前沿应用的完整代码实现。\n\n面对 AI 技术迭代快、论文复现难的问题，learnopencv 提供了经过验证的落地方案，帮助用户跨越从“读懂原理”到“写出代码”的鸿沟。无论是实时目标检测（如最新的 YOLO26、RF-DETR）、多目标跟踪、人脸隐私保护，还是大模型部署（如 Jetson 边缘计算、vLLM 服务）、3D 重建（SAM 3D、高斯泼溅）以及 RAG 检索增强生成等热门方向，这里都能找到对应的演示项目。\n\n该资源特别适合开发者、算法研究人员及 AI 学习者使用。对于希望提升工程能力的程序员，它提供了生产级的参考架构；对于科研人员，它是快速验证新想法的试验田；对于初学者，则是循序渐进掌握 OpenCV 与深度学习框架的最佳实践指南。通过涵盖从传统图像处理到大模型应用的全栈内容，learnopencv 致力于让每个人都能轻松上手并精通 AI 开发。","# LearnOpenCV\n\nThis repository contains code for Computer Vision, Deep learning, and AI research articles shared on our blog [LearnOpenCV.com](https:\u002F\u002Fwww.LearnOpenCV.com).\n\nWant to become an expert in AI? [AI Courses by OpenCV](https:\u002F\u002Fopencv.org\u002Fcourses\u002F) is a great place to start.\n\n\u003Ca href=\"https:\u002F\u002Fopencv.org\u002Fcourses\u002F\">\n\n\u003Cp align=\"center\">\n\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fspmallick_learnopencv_readme_25bc0f803b08.png\">\n\u003C\u002Fp>\n\u003C\u002Fa>\n\n## List of Blog Posts\n\n| Blog Post | Code|\n| ------------- |:-------------|\n| [RF-DETR Segmentation: Real-Time Detection & Instance Segmentation Guide](https:\u002F\u002Flearnopencv.com\u002Frf-detr-segmentation-real-time-detection-instance-segmentation-guide\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FRF_DETR_Segmentation_Demo) |\n| [YOLO26 Instance Segmentation: Pixel-Perfect AI at Real-Time Speed](https:\u002F\u002Flearnopencv.com\u002Fyolo26-instance-segmentation-pixel-perfect-ai-at-real-time-speed\u002F) | [Code](YOLO26-instance-segmentation\u002F) |\n| [Multi-Object Tracking with Roboflow Trackers and OpenCV](https:\u002F\u002Flearnopencv.com\u002Fmulti-object-tracking-with-roboflow-trackers-and-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FRoboflow_Trackers_Demo) |\n| [Real-Time Face Blur and Pixelation with OpenCV YuNet](https:\u002F\u002Flearnopencv.com\u002Fface-blur-pixelation-opencv-yunet\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFaceBlurPixelate) |\n| [Breaking the Bottleneck: Achieving Native NMS-Free Inference with YOLO26](https:\u002F\u002Flearnopencv.com\u002Fyolo26-nms-free-inference\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLO26-NMS-Free-Demo) |\n| [YOLOv26: An Object Detector Built for Real-Time Deployment](https:\u002F\u002Flearnopencv.com\u002Fyolov26-real-time-deployment\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FInference_RF-DETR_YOLO26_RT-DETR) |\n| [Beyond Transformers: A Deep Dive into HOPE](https:\u002F\u002Flearnopencv.com\u002Fhope-beyond-transformers\u002F) | |\n| [Serving SGLang: Launch a Production-Style Server](https:\u002F\u002Flearnopencv.com\u002Fsglang-a-production-server\u002F) | |\n|[Deployment on Edge: LLM Serving on Jetson using vLLM](https:\u002F\u002Flearnopencv.com\u002Fdeployment-on-edge-vllm-on-jetson\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDeployment-on-Edge-LLM-Serving-on-Jetson-using-vLLM)|\n|[Nested Learning: Is Deep Learning Architecture an Illusion?](https:\u002F\u002Flearnopencv.com\u002Fnested-learning\u002F)||\n| [How to Build a GitHub Code-Analyser Agent for Developer Productivity](https:\u002F\u002Flearnopencv.com\u002Fhow-to-build-a-github-code-analyser-agent\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHow_to_Build_a_GitHub_Code_Analyser_Agent_for_Developer_Productivity) |\n| [The Existential Problems in LLM Serving](https:\u002F\u002Flearnopencv.com\u002Fthe-existential-problems-in-llm-serving\u002F) | |\n| [SAM 3D: Foundation Model for Single-Image 3D Reconstruction](https:\u002F\u002Flearnopencv.com\u002Fsam-3d\u002F) | |\n| [SAM-3: What’s New, How It Works, and Why It Matters](https:\u002F\u002Flearnopencv.com\u002Fsam-3-whats-new\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSAM-3) |\n| [Image-GS: Adaptive Image Reconstruction using 2D Gaussians](https:\u002F\u002Flearnopencv.com\u002Fimage-gs-image-reconstruction-using-2d-gaussians\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImage_GS_Adaptive_Image_Reconstruction_using_2D_Gaussians) |\n| [Ultimate Guide to Vector Databases and RAG Pipeline](https:\u002F\u002Flearnopencv.com\u002Fvector-db-and-rag-pipeline-for-document-rag\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FUltimate_Guide_to_Vector_Databases_and_RAG_pipeline) |\n|[What Makes DeepSeek OCR So Powerful](https:\u002F\u002Flearnopencv.com\u002Fwhat-makes-deepseek-ocr-so-powerful\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FWhat-Makes-DeepSeek-OCR-So-Powerful)|\n| [2D Gaussian Splatting: Geometrically Accurate Radiance Field Reconstruction](https:\u002F\u002Flearnopencv.com\u002F2d-gaussian-splatting-2dgs\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002F2D_Gaussian_Splatting_Geometrically_Accurate_Radiance_Field_Reconstruction) |\n| [TRM: Tiny Recursive Models](https:\u002F\u002Flearnopencv.com\u002Ftrm-tiny-ai-models-outsmarting-giants-on-complex-puzzles\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTRM) |\n|[Deploying ML Models on Arduino: From Blink to Think](https:\u002F\u002Flearnopencv.com\u002Fdeploying-ml-on-arduino\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDeploying-ML-Models-on-Arduino-From-Blink-to-Think)|\n| [VideoRAG: Redefining Long-Context Video Comprehension](https:\u002F\u002Flearnopencv.com\u002Fvideorag-long-context-video-comprehension\u002F) | |\n| [AI Agent in Action: Automating Desktop Tasks with VLMs](https:\u002F\u002Flearnopencv.com\u002Fbuild-ai-agents-using-vlm\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FLocal-VLM-Agents-in-Action-GUI-Automation-with-Moondream3-and-Gemini) |\n| [Top VLM Evaluation Metrics for Optimal Performance Analysis](https:\u002F\u002Flearnopencv.com\u002Fvlm-evaluation-metrics\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVLM_Evaluation_Metrics) |\n|[Getting Started with VLM on Jetson Nano](https:\u002F\u002Flearnopencv.com\u002Fvlm-on-jetson-nano\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FGetting-Started-with-VLM-on-Jetson-Nano)|\n| [VLM on Edge: Worth the Hype or Just a Novelty?](https:\u002F\u002Flearnopencv.com\u002Fvlm-on-edge-devices\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVLM-on-Edge-Worth-the-Hype-or-Just-a-Novelty) |\n| [AnomalyCLIP : Harnessing CLIP for Weakly-Supervised Video Anomaly Recognition](https:\u002F\u002Flearnopencv.com\u002Fanomalyclip-video-anomaly-recognition\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAnomalyCLIP_Harnessing_CLIP_for_Weakly_Supervised_Video_Anomaly_Recognition) |\n| [AI_for_Video_Understanding_From_Content_Moderation_to_Summarization](https:\u002F\u002Flearnopencv.com\u002Fai-for-video-understanding\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAI_for_Video_Understanding_From_Content_Moderation_to_Summarization) |\n| [Video-RAG: Training-Free Retrieval for Long-Video LVLMs](https:\u002F\u002Flearnopencv.com\u002Fvideo-rag-for-long-videos\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVideo-RAG_Training_Free_Retrieval_for_Long_Video_LVLMs) |\n| [Object Detection and Spatial Understanding with VLMs ft. Qwen2.5-VL](https:\u002F\u002Flearnopencv.com\u002Fobject-detection-with-vlms-ft-qwen2-5-vl\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fobject-detection-with-vlms) |\n| [LangGraph: Building Self-Correcting RAG Agent for Code Generation](https:\u002F\u002Flearnopencv.com\u002Flanggraph-self-correcting-agent-code-generation\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FLangGraph_Building_Self_Correcting_RAG_Agent_for_Code_Generation) |\n| [Inside Sinusoidal Position Embeddings: A Sense of Order](https:\u002F\u002Flearnopencv.com\u002Fsinusoidal-position-embeddings\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSinusoidal_Position_Embeddings) |\n| [Inside RoPE: Rotary Magic into Position Embeddings](https:\u002F\u002Flearnopencv.com\u002Frope-position-embeddings\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FInside_RoPE_Position_Embeddings) |\n| [SimLingo-Vision-Language-Action-Model-for-Autonomous-Driving](https:\u002F\u002Flearnopencv.com\u002Fsimlingo-vision-language-action-model-for-autonomous-driving\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSimLingo-Vision-Language-Action-Model-for-Autonomous-Driving) |\n| [FineTuning Gemma 3n for Medical VQA on ROCOv2](https:\u002F\u002Flearnopencv.com\u002Ffinetuning-gemma-3n-medical-vqa\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Ffinetuning-gemma3n) |\n| [SmolLM3 Blueprint: SOTA 3B-Parameter LLM](https:\u002F\u002Flearnopencv.com\u002Fsmollm3-explained\u002F) | |\n| [LangGraph-A-Visual-Automation-and-Summarization-Pipeline](https:\u002F\u002Flearnopencv.com\u002Flanggraph-building-a-visual-web-browser-agent\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FLangGraph-A-Visual-Automation-and-Summarization-Pipeline) |\n| [Fine-Tuning AnomalyCLIP: Class-Agnostic Zero-Shot Anomaly Detection](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-anomalyclip-medical-anomaly-clip\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-AnomalyCLIP) |\n| [SigLIP 2: DeepMind’s Multilingual Vision-Language Model](https:\u002F\u002Flearnopencv.com\u002Fsiglip-2-deepminds-multilingual-vision-language-model\u002F) | |\n| [MedGemma: Google’s Medico VLM for Clinical QA, Imaging, and More](https:\u002F\u002Flearnopencv.com\u002Fmedgemma-explained\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fmedgemma) |\n| [Nanonets-OCR-s: Enabling Rich, Structured Markdown for Document Understanding](https:\u002F\u002Flearnopencv.com\u002Fnanonets-ocr-s\u002F) | |\n| [Optimizing VJEPA-2: Tackling Latency & Context in Real-Time Video Classification Scripts](https:\u002F\u002Flearnopencv.com\u002Foptimizing-vjepa-2-in-real-time-video-classification\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVJEPA-2-Video-Classification) |\n| [V-JEPA 2: Meta’s Breakthrough in AI for the Physical World](https:\u002F\u002Flearnopencv.com\u002F?p=73731&preview_id=73731&preview_nonce=beb70ccf8e&preview=true#heading-7) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FV-JEPA-2) |\n| [NVIDIA Cosmos Reason1: Video Understanding](https:\u002F\u002Flearnopencv.com\u002Fcosmos-reason-vlm-video-vqa\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FCosmos-Reason1-Video-Understanding) |\n| [GR00T N1.5 Explained](https:\u002F\u002Flearnopencv.com\u002Fgr00t-n1_5-explained\u002F) |  |\n| [LLaVA](https:\u002F\u002Flearnopencv.com\u002Fllava-training-a-visual-assistant\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FLLaVA) |\n| [SmolVLA: Affordable & Efficient VLA Robotics on Consumer GPUs](https:\u002F\u002Flearnopencv.com\u002Fsmolvla-lerobot-vision-language-action-model\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fsmolvla) |\n| [Fine-Tuning Grounding DINO: Open-Vocabulary Object Detection](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-grounding-dino\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-Grounding-DINO-Open-Vocabulary-Object-Detection) |\n| [Getting Started with Qwen3 – The Thinking Expert](https:\u002F\u002Flearnopencv.com\u002Fqwen3\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fqwen3) |\n| [Inside the GPU: A Comprehensive Guide to Modern Graphics Architecture](https:\u002F\u002Flearnopencv.com\u002Fmodern-gpu-architecture-explained\u002F) | |\n| [Distributed Parallel Training: PyTorch](https:\u002F\u002Flearnopencv.com\u002Fdistributed-parallel-training-pytorch-multi-gpu-setup\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDistributed-Training-PyTorch) |\n| [MONAI: The Definitive Framework for Medical Imaging Powered by PyTorch](https:\u002F\u002Flearnopencv.com\u002Fmonai-medical-imaging-pytorch\u002F) | |\n| [SANA-Sprint: The One-Step Revolution in High-Quality AI Image Synthesis](https:\u002F\u002Flearnopencv.com\u002Fsana-sprint-the-one-step-revolution-in-high-quality-ai-image-synthesis\u002F) | |\n| [FramePack-Video-Diffusion-but-feels-like-Image-Diffusion](https:\u002F\u002Flearnopencv.com\u002Fframepack-video-diffusion\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFramePack-Video-Diffusion-but-feels-like-Image-Diffusion) |\n| [Model Weights File Formats in Machine Learning](https:\u002F\u002Flearnopencv.com\u002Fmodel-weights-file-formats-in-machine-learning\u002F) | |\n| [Unsloth: A Guide from Basics to Fine-Tuning Vision Models](https:\u002F\u002Flearnopencv.com\u002Funsloth-guide-efficient-llm-fine-tuning\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FUnsloth_A_Guide_From_Basics_to_Fine_Tuning_Vision_Models) |\n| [Iterative Closest Point (ICP) Algorithm Explained](https:\u002F\u002Flearnopencv.com\u002Fiterative-closest-point-icp-explained\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FIterative-Closest-Point-ICP) |\n| [MedSAM2 Explained: One Prompt to Segment Anything in Medical Imaging](https:\u002F\u002Flearnopencv.com\u002Fmedsam2-explained\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002Fmedsam2-explained) |\n| [Batch Normalization and Dropout as Regularizers](https:\u002F\u002Flearnopencv.com\u002Fbatch-normalization-and-dropout-as-regularizers\u002F) | |\n| [DINOv2_by_Meta_A_Self-Supervised_foundational_vision_model](https:\u002F\u002Flearnopencv.com\u002Fdinov2-self-supervised-vision-transformer\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FDINOv2_by_Meta_A_Self-Supervised_foundational_vision_model) |\n| [Beginner's Guide to Embedding Models](https:\u002F\u002Flearnopencv.com\u002Fembedding-models-explained\u002F) | |\n| [MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors](https:\u002F\u002Flearnopencv.com\u002Fmast3r-slam-realtime-dense-slam-explained\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FMASt3R-SLAM) |\n| [Google's A2A Protocol](https:\u002F\u002Flearnopencv.com\u002Fgoogles-a2a-protocol-heres-what-you-need-to-know\u002F) | |\n| [Nvidia SANA : Faster Image Generation](https:\u002F\u002Flearnopencv.com\u002Fnvidia-sana-image-generation-model\u002F) | |\n| [Fine-tuning RF-DETR](https:\u002F\u002Flearnopencv.com\u002Frf-detr-object-detection\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FFine-tuning-RF-DETR) |\n| [Qwen2.5-Omni: A Real-Time Multimodal AI](https:\u002F\u002Flearnopencv.com\u002Fqwen2.5-omni\u002F) | |\n| [Vision Language Action Models: Robotic Control](https:\u002F\u002Flearnopencv.com\u002Fvision-language-action-models-lerobot-policy\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVision-Language-Action-Models) |\n| [Fine-Tuning Gemma 3 VLM using QLoRA for LaTeX-OCR Dataset](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-gemma-3\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-Gemma-3-VLM-using-QLoRA-for-LaTeX-OCR-Dataset) |\n| [ComfyUI](https:\u002F\u002Flearnopencv.com\u002Fintroduction-to-comfyui-for-stable-diffusion\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FComfyUI) |\n| [Gemma-3: A Comprehensive Introduction](https:\u002F\u002Flearnopencv.com\u002Fgemma-3\u002F) | |\n| [YOLO11 on Raspberry Pi: Optimizing Object Detection for Edge Devices](https:\u002F\u002Flearnopencv.com\u002Fyolo11-on-raspberry-pi\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fyolo11-on-raspberry-pi) |\n| [VGGT: Visual Geometry Grounded Transformer – For Dense 3D Reconstruction](https:\u002F\u002Flearnopencv.com\u002Fvggt-visual-geometry-grounded-transformer-3d-reconstruction\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVGGT-3D-Reconstruction) |\n| [DDIM: The Faster, Improved Version of DDPM for Efficient AI Image Generation](https:\u002F\u002Flearnopencv.com\u002Funderstanding-ddim\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDDIM-The-Faster-Improved-Version-of-DDPM-for-Efficient-AI-Image-Generation) |\n| [Introduction to Model Context Protocol (MCP)](https:\u002F\u002Flearnopencv.com\u002Fintroduction-to-model-context-protocol\u002F) | |\n| [MASt3R and MASt3R-SfM Explanation: Image Matching and 3D Reconstruction](https:\u002F\u002Flearnopencv.com\u002Fmast3r-sfm-grounding-image-matching-3d\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMASt3R-SfM-3D-Reconstruction-Image-Matching) |\n| [MatAnyone Explained: Consistent Memory for Better Video Matting](https:\u002F\u002Flearnopencv.com\u002Fmatanyone-for-better-video-matting\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMatAnyone-Explained-Consistent-Memory-for-Better-Video-Matting) |\n| [GraphRAG: For Medical Document Analysis](https:\u002F\u002Flearnopencv.com\u002Fgraphrag-explained-knowledge-graphs-medical\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FGraphrag-Medical-Document-Analysis) |\n| [OmniParser: Vision Based GUI Agent](https:\u002F\u002Flearnopencv.com\u002Fomniparser-vision-based-gui-agent\u002F) | |\n| [Fine-Tuning-YOLOv12-Comparison-With-YOLOv11-And-YOLOv7-Based-Darknet](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-yolov12\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-YOLOv12-Comparison-With-YOLOv11-And-YOLOv7-Based-Darknet) |\n| [FineTuning RetinaNet for Wildlife Detection with PyTorch: A Step-by-Step Tutorial](https:\u002F\u002Flearnopencv.com\u002Ffinetuning-retinanet) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Ffinetuning-retinanet) |\n| [DUSt3R: Geometric 3D Vision Made Easy :  Explanation and Results](https:\u002F\u002Flearnopencv.com\u002Fdust3r-geometric-3d-vision\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDUSt3R-Dense-3D-Reconstruction) |\n| [YOLOv12: Attention Meets Speed](https:\u002F\u002Flearnopencv.com\u002Fyolov12) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv12) |\n| [Video Generation: A Diffusion based approach](https:\u002F\u002Flearnopencv.com\u002Fvideo-generation-models\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVideo-Generation-A-Diffusion-based-approach) |\n| [Agentic AI: A Comprehensive Introduction](https:\u002F\u002Flearnopencv.com\u002Fagentic-ai\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAgentic-AI-A-Comprehensive-Introduction) |\n| [Finetuning SAM2 for Leaf Disease Segmentation](https:\u002F\u002Flearnopencv.com\u002Ffinetuning-sam2\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Ffinetuning-sam2) |\n| [Object Insertion in Gaussian Splatting: Paper Explained and Training Code for MCMC and Bilateral Grid](https:\u002F\u002Flearnopencv.com\u002Fobject-insertion-in-gaussian-splatting\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FObject-Insertion-in-Gaussian-Splatting) |\n| [Depth Pro: Sharp Monocular Metric Depth](https:\u002F\u002Flearnopencv.com\u002Fdepth-pro-monocular-metric-depth) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDepthPro-Monocular-Metric-Depth) |\n| [Fine-tuning-Stable-Diffusion-3_5-UI-images](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-stable-diffusion-3-5m\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-tuning-Stable-Diffusion-3_5-UI-images) |\n| [SimSiam: Streamlining SSL with Stop-Gradient Mechanism](https:\u002F\u002Flearnopencv.com\u002Fsimsiam\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSimSiam-Streamlining-SSL-with-Stop-Gradient-Mechanism) |\n| [Image Captioning using ResNet and LSTM](https:\u002F\u002Flearnopencv.com\u002Fimage-captioning\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImage-Captioning-using-ResNet-and-LSTM) |\n| [Molmo VLM: Paper Explanation and Demo](https:\u002F\u002Flearnopencv.com\u002Fmolmo-vlm) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMolmo-VLM-SAM2) |\n| [3D Gaussian Splatting Paper Explanation: Training Custom Datasets with NeRF-Studio Gsplats](https:\u002F\u002Flearnopencv.com\u002F3d-gaussian-splatting\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002F3D-Gaussian-Splatting-Code) |\n| [FLUX Image Generation: Experimenting with the Parameters](https:\u002F\u002Flearnopencv.com\u002Fflux-ai-image-generator\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFlux-Image-Generation) |\n| [Contrastive-Learning-SimCLR-and-BYOL(With Code Example)](https:\u002F\u002Flearnopencv.com\u002Fcontrastive-learning-simclr-and-byol-with-code-example\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FContrastive-Learning-SimCLR-and-BYOL) |\n| [The Annotated NeRF : Training on Custom Dataset from Scratch in Pytorch](https:\u002F\u002Flearnopencv.com\u002Fannotated-nerf-pytorch\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAnnotated-NeRF) |\n| [Stable Diffusion 3 and 3.5: Paper Explanation and Inference](https:\u002F\u002Flearnopencv.com\u002Fstable-diffusion-3\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FStable-Diffusion-3) |\n| [LightRAG - Legal Document Analysis](https:\u002F\u002Flearnopencv.com\u002Flightrag\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FLightRAG-Legal) |\n| [NVIDIA AI Summit 2024 – India Overview](https:\u002F\u002Flearnopencv.com\u002Fnvidia-ai-summit-2024-india-overview\u002F) | |\n| [Introduction to Speech to Speech: Most Efficient Form of NLP](https:\u002F\u002Flearnopencv.com\u002Fspeech-to-speech\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fspeech-to-speech) |\n| [Training 3D U-Net for Brain Tumor Segmentation (BraTS-GLI)](https:\u002F\u002Flearnopencv.com\u002F3d-u-net-brats\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTraining_3D_U-Net_Brain_Tumor_Seg) |\n| [DETR: Overview and Inference](https:\u002F\u002Flearnopencv.com\u002Fdetr-overview-and-inference\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDETR-Overview_and_Inference) |\n| [YOLO11: Faster Than You Can Imagine!](https:\u002F\u002Flearnopencv.com\u002Fyolo11\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLO11) |\n| [Exploring DINO: Self-Supervised Transformers for Road Segmentation with ResNet50 and U-Net](https:\u002F\u002Flearnopencv.com\u002Ffine-tune-dino-self-supervised-learning-segmentation\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FExploring-DINO-dino-road-segmentation) |\n| [Sapiens: Foundation for Human Vision Models by Meta](https:\u002F\u002Flearnopencv.com\u002Fsapiens-human-vision-models) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSapiens-Human-Vision-Model-Meta) |\n| [Multimodal RAG with ColPali and Gemini](https:\u002F\u002Flearnopencv.com\u002Fmultimodal-rag-with-colpali) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMultimodal-RAG-with-ColPali-Gemini) |\n| [Building Autonomous Vehicle in Carla: Path Following with PID Control & ROS 2](https:\u002F\u002Flearnopencv.com\u002Fpid-controller-ros-2-carla\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FBuilding_Autonomous_Vehicle_in_Carla_Path_Following_with_PID_Control_ROS2) |\n| [Handwritten Text Recognition using OCR](https:\u002F\u002Flearnopencv.com\u002Fhandwritten-text-recognition-using-ocr\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHandwritten_Text_Recognition_using_OCR) |\n| [Training CLIP from Sratch for Image Retrieval](https:\u002F\u002Flearnopencv.com\u002Fclip-model) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTraining-CLIP-from-Scratch-for-Image-Retrieval) |\n| [Introduction to LiDAR SLAM: LOAM and LeGO-LOAM Paper and Code Explanation with ROS 2 Implementation](https:\u002F\u002Flearnopencv.com\u002Flidar-slam-with-ros2) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FLeGO-LOAM-ROS2) |\n| [Recommendation System using Vector Search](https:\u002F\u002Flearnopencv.com\u002Frecommendation-system-using-vector-search) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FRecommendation-System-using-Vector-Search) |\n| [Fine Tuning Whisper on Custom Dataset](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-whisper-on-custom-dataset\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-Whisper-on-Custom-Dataset) |\n| [SAM 2 – Promptable Segmentation for Images and Videos](https:\u002F\u002Flearnopencv.com\u002Fsam-2\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSAM_2_Segment_Anything_Model_2) |\n| [Introduction to Feature Matching Using Neural Networks](https:\u002F\u002Flearnopencv.com\u002Ffeature-matching\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFeature-Matching-Using-Neural-Networks) |\n| [Introduction to ROS2 (Robot Operating System 2): Tutorial on ROS2 Working, DDS, ROS1 RMW, Topics, Nodes, Publisher, Subscriber in Python](https:\u002F\u002Flearnopencv.com\u002Frobot-operating-system-introduction) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntroduction-to-ROS2-in-python) |\n| [CVPR 2024 Research Papers - Part- 2](https:\u002F\u002Flearnopencv.com\u002Fcvpr-2024-research-papers) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fcvpr-2024-research-papers-part2) |\n| [CVPR 2024: An Overview and Key Papers](https:\u002F\u002Flearnopencv.com\u002Fcvpr2024\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FCVPR-2024) |\n| [Object Detection on Edge Device - OAK-D-Lite](https:\u002F\u002Flearnopencv.com\u002Fobject-detection-on-edge-device) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FObject-Detection-on-Edge-Devices) |\n| [Fine-Tuning YOLOv10 Models on Custom Dataset](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-yolov10\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-YOLOv10-Models-Custom-Dataset) |\n| [ROS2 and Carla Setup Guide for Ubuntu 22.04](https:\u002F\u002Flearnopencv.com\u002Fros2-and-carla-setup-guide\u002F) |  |\n| [Understanding Visual SLAM for Robotics Perception: Building Monocular SLAM from Scratch in Python](https:\u002F\u002Flearnopencv.com\u002Fmonocular-slam-in-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMonocular%20SLAM%20for%20Robotics%20implementation%20in%20python) |\n| [Enhancing Image Segmentation using U2-Net: An Approach to Efficient Background Removal](https:\u002F\u002Flearnopencv.com\u002Fu2-net-image-segmentation\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FEfficient-Background-Removal-using-U2-Net) |\n| [YOLOv10: The Dual-Head OG of YOLO Series](https:\u002F\u002Flearnopencv.com\u002Fyolov10\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv10) |\n| [Fine-tuning Faster R-CNN on Sea Rescue Dataset](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-faster-r-cnn\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-tuning-Faster-R-CNN-on-SeaRescue-Dataset) |\n| [Mastering Recommendation System: A Complete Guide](https:\u002F\u002Flearnopencv.com\u002Frecommendation-system\u002F) | |\n| [Automatic Speech Recognition with Diarization : Speech-to-Text](https:\u002F\u002Flearnopencv.com\u002Fautomatic-speech-recognition\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAutomatic-Speech-Recognition-with-Diarization-Speech-to-Text) |\n| [Building MobileViT Image Classification Model from Scratch In Keras 3](https:\u002F\u002Flearnopencv.com\u002Fmobilevit-keras-3\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FBuilding%20MobileViT%20from%20Scratch%20in%20Keras%203) |\n| [SDXL Inpainting: Fusing Image Inpainting with Stable Diffusion](https:\u002F\u002Flearnopencv.com\u002Fsdxl-inpainting\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSDXL-inpainting) |\n| [YOLOv9 Instance Segmentation on Medical Dataset](https:\u002F\u002Flearnopencv.com\u002Fyolov9-instance-segmentation-on-medical-dataset\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv9-Instance-Segmentation-on-Medical-Dataset) |\n| [A Comprehensive Guide to Robotics](https:\u002F\u002Flearnopencv.com\u002Fa-comprehensive-guide-to-robotics\u002F) | |\n| [Integrating Gradio with OpenCV DNN](https:\u002F\u002Flearnopencv.com\u002Fintegrating-gradio-with-opencv-dnn\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntegrating-Gradio-with-OpenCV-DNN) |\n| [Fine-Tuning YOLOv9 on Custom Dataset](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-yolov9\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-YOLOv9-Models-Custom-Dataset) |\n| [Dreambooth using Diffusers](https:\u002F\u002Flearnopencv.com\u002Fdreambooth-using-diffusers\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDreambooth_using_Diffusers) |\n| [Introduction to Hugging Face Diffusers](https:\u002F\u002Flearnopencv.com\u002Fhugging-face-diffusers\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntroduction_to_Diffusers) |\n| [Introduction to Ultralytics Explorer API](https:\u002F\u002Flearnopencv.com\u002Fultralytics-explorer-api\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntroduction-to-Ultralytics-Explorer-API) |\n| [YOLOv9: Advancing the YOLO Legacy](https:\u002F\u002Flearnopencv.com\u002Fyolov9-advancing-the-yolo-legacy\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv9-Advancing-the-YOLO-Legacy) |\n| [Fine-Tuning LLMs using PEFT](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-llms-using-peft\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-LLMs-using-PEFT) |\n| [Depth Anything: Accelerating Monocular Depth Perception](https:\u002F\u002Flearnopencv.com\u002Fdeciphering-llms\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDepth-Anything) |\n| [Deciphering LLMs: From Transformers to Quantization](https:\u002F\u002Flearnopencv.com\u002Fdeciphering-llms\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDeciphering-LLMs) |\n| [YOLO Loss Function Part 2: GFL and VFL Loss](https:\u002F\u002Flearnopencv.com\u002Fyolo-loss-function-gfl-vfl-loss\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLO-Loss-Functions-Part2) |\n| [YOLOv8-Object-Tracking-and-Counting-with-OpenCV](https:\u002F\u002Flearnopencv.com\u002Fyolov8-object-tracking-and-counting-with-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv8-Object-Tracking-and-Counting-with-OpenCV) |\n| [Stereo Vision in ADAS: Pioneering Depth Perception Beyond LiDAR](https:\u002F\u002Flearnopencv.com\u002Fadas-stereo-vision\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FADAS-Stereo-Vision) |\n| [YOLO Loss Function Part 1: SIoU and Focal Loss](https:\u002F\u002Flearnopencv.com\u002Fyolo-loss-function-siou-focal-loss\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLO-Loss-Functions-Part1) |\n| [Moving Object Detection with OpenCV](https:\u002F\u002Flearnopencv.com\u002Fmoving-object-detection-with-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMoving-Object-Detection-with-OpenCV) |\n| [Integrating ADAS with Keypoint Feature Pyramid Network for 3D LiDAR Object Detection](https:\u002F\u002Flearnopencv.com\u002F3d-lidar-object-detection\u002F) | [Code](https:\u002F\u002Fwww.dropbox.com\u002Fscl\u002Ffi\u002F3n1s68jtfkjmw2f5e5ctv\u002F3D-LiDAR-Object-Detection.zip?rlkey=d8q6xvlxis4oxso4qki87omvc&dl=1) |\n| [Mastering All YOLO Models from YOLOv1 to YOLO-NAS: Papers Explained (2024)](https:\u002F\u002Flearnopencv.com\u002Fmastering-all-yolo-models) | |\n| [GradCAM: Enhancing Neural Network Interpretability in the Realm of Explainable AI](https:\u002F\u002Flearnopencv.com\u002Fintro-to-gradcam\u002F) | [Code](https:\u002F\u002Fwww.dropbox.com\u002Fscl\u002Ffo\u002F3p3sg5fnvhrvi9vp00i0w\u002Fh?rlkey=1x01uz5o7esex7p6c8r534iyn&dl=1) |\n| [Text Summarization using T5: Fine-Tuning and Building Gradio App](https:\u002F\u002Flearnopencv.com\u002Ftext-summarization-using-t5\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FText-Summarization-using-T5-Fine-Tuning-and-Building-Gradio-App) |\n| [3D LiDAR Visualization using Open3D: A Case Study on 2D KITTI Depth Frames for Autonomous Driving](https:\u002F\u002Flearnopencv.com\u002F3d-lidar-visualization\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002F3D-LiDAR-Perception) |\n| [Fine Tuning T5: Text2Text Transfer Transformer for Building a Stack Overflow Tag Generator](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-t5\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-T5-Text2Text-Transformer-for-Strack-Overflow-Tag-Generation) |\n| [SegFormer 🤗 : Fine-Tuning for Improved Lane Detection in Autonomous Vehicles](https:\u002F\u002Flearnopencv.com\u002Fsegformer-fine-tuning-for-lane-detection) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-SegFormer-For-Lane-Detection) |\n| [Fine-Tuning BERT using Hugging Face Transformers](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-bert) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-BERT-using-Hugging-Face-Transformers) |\n| [YOLO-NAS Pose](https:\u002F\u002Flearnopencv.com\u002Fyolo-nas-pose) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLO-NAS-Pose) |\n| [BERT: Bidirectional Encoder Representations from Transformers](https:\u002F\u002Flearnopencv.com\u002Fbert-bidirectional-encoder-representations-from-transformers\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FBERT-Bidirectional-Encoder-Representations-from-Transformers) |\n| [Comparing KerasCV YOLOv8 Models on the Global Wheat Data 2020](https:\u002F\u002Flearnopencv.com\u002Fcomparing-kerascv-yolov8-models\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FComparing-KerasCV-YOLOv8-Models-on-the-Global-Wheat-Data-2020) |\n| [Top 5 AI papers of September 2023](https:\u002F\u002Flearnopencv.com\u002Ftop-5-ai-papers-of-september-2023\u002F) | |\n| [Empowering Drivers: The Rise and Role of Advanced Driver Assistance Systems](https:\u002F\u002Flearnopencv.com\u002Fadvanced-driver-assistance-systems\u002F) | |\n| [Semantic Segmentation using KerasCV DeepLabv3+](https:\u002F\u002Flearnopencv.com\u002Fkerascv-deeplabv3-plus-semantic-segmentation\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSemantic-Segmentation-using-KerasCV-with-DeepLabv3-Plus) |\n| [Object Detection using KerasCV YOLOv8](https:\u002F\u002Flearnopencv.com\u002Fobject-detection-using-kerascv-yolov8\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FObject-Detection-using-KerasCV-YOLOv8) |\n| [Fine-tuning YOLOv8 Pose Models for Animal Pose Estimation](https:\u002F\u002Flearnopencv.com\u002Fanimal-pose-estimation\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-tuning-YOLOv8-Pose-Models-for-Animal-Pose-Estimation) |\n| [Top 5 AI papers of August 2023](https:\u002F\u002Flearnopencv.com\u002Ftop-5-ai-papers-of-august-2023\u002F) | |\n| [Fine Tuning TrOCR - Training TrOCR to Recognize Curved Text](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-trocr-training-trocr-to-recognize-curved-text\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-TrOCR) |\n| [TrOCR - Getting Started with Transformer Based OCR](https:\u002F\u002Flearnopencv.com\u002Ftrocr-getting-started-with-transformer-based-ocr\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTrOCR-Getting-Started-with-Transformer-Based-OCR) |\n| [Facial Emotion Recognition](https:\u002F\u002Flearnopencv.com\u002Ffacial-emotion-recognition\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFacial-Emotion-Recognition) |\n| [Object Keypoint Similarity in Keypoint Detection](https:\u002F\u002Flearnopencv.com\u002Fobject-keypoint-similarity\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FObject-Keypoint-Similarity-in-Keypoint-Detection) |\n| [Real Time Deep SORT with Torchvision Detectors](https:\u002F\u002Flearnopencv.com\u002Freal-time-deep-sort-with-torchvision-detectors\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FReal_Time_Deep_SORT_using_Torchvision_Detectors) |\n| [Top 5 AI papers of July 2023](https:\u002F\u002Flearnopencv.com\u002Ftop-5-ai-papers-of-july-2023\u002F) | |\n| [Medical Image Segmentation](https:\u002F\u002Flearnopencv.com\u002Fmedical-image-segmentation\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMedical-Image-Segmentation-Using-HuggingFace-&-PyTorch) |\n| [Weighted Boxes Fusion in Object Detection: A Comparison with Non-Maximum Suppression](https:\u002F\u002Flearnopencv.com\u002Fweighted-boxes-fusion\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FWeighted-Boxes-Fusion-in-Object-Detection) |\n| [Medical Multi-label Classification with PyTorch & Lightning](https:\u002F\u002Flearnopencv.com\u002Fmedical-multi-label\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMedical_Multi-label_Classification_with_PyTorch_&_Lightning) |\n| [Getting Started with PaddlePaddle: Exploring Object Detection, Segmentation, and Keypoints](https:\u002F\u002Flearnopencv.com\u002Fpaddlepaddle\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntroduction-to-PaddlePaddle) |\n| [Drone Programming With Computer Vision A Beginners Guide](https:\u002F\u002Flearnopencv.com\u002Fdrone-programming-with-computer-vision\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDrone-Programming-With-Computer-Vision-A-Beginners-Guide) |\n| [How to Build a Pip Installable Package & Upload to PyPi](https:\u002F\u002Flearnopencv.com\u002Fbuilding-pip-installable-package-pypi\u002F) | |\n| [IoU Loss Functions for Faster & More Accurate Object Detection](https:\u002F\u002Flearnopencv.com\u002Fiou-loss-functions-object-detection\u002F) | |\n| [Exploring Slicing Aided Hyper Inference for Small Object Detection](https:\u002F\u002Flearnopencv.com\u002Fslicing-aided-hyper-inference\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FExploring-Slicing-Aided-Hyper-Inference) |\n| [Advancements in Face Recognition Models, Toolkit and Datasets](https:\u002F\u002Flearnopencv.com\u002Fface-recognition-models\u002F) | |\n| [Train YOLO NAS on Custom Dataset](https:\u002F\u002Flearnopencv.com\u002Ftrain-yolo-nas-on-custom-dataset\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTrain-YOLO-NAS-on-Custom-Dataset) |\n| [Train YOLOv8 Instance Segmentation on Custom Data](https:\u002F\u002Flearnopencv.com\u002Ftrain-yolov8-instance-segmentation\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTrain-YOLOv8-Instance-Segmentation-on-Custom-Data) |\n| [YOLO-NAS: New Object Detection Model Beats YOLOv6 & YOLOv8](https:\u002F\u002Flearnopencv.com\u002Fyolo-nas\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLO-NAS_Introduction) |\n| [Segment Anything – A Foundation Model for Image Segmentation](https:\u002F\u002Flearnopencv.com\u002Fsegment-anything\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSegment-Anything-A-Foundation-Model-for-Image-Segmentation) |\n|[Build a Video to Slides Converter Application using the Power of Background Estimation and Frame Differencing in OpenCV](https:\u002F\u002Flearnopencv.com\u002Fvideo-to-slides-converter-using-background-subtraction\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FBuild-a-Video-to-Slides-Converter-Application-using-the-Power-of-Background-Estimation-and-Frame-Differencing-in-OpenCV)|\n|[A Closer Look at CVAT: Perfecting Your Annotations](https:\u002F\u002Flearnopencv.com\u002Fa-closer-look-at-cvat-perfecting-your-annotations\u002F)|[YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=yxX_0-zr-2U&list=PLfYPZalDvZDLvFhjuflhrxk_lLplXUqqB)|\n| [ControlNet - Achieving Superior Image Generation Results](https:\u002F\u002Flearnopencv.com\u002Fcontrolnet\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FControlNet-Achieving-Superior-Image-Generation-Results) |\n| [InstructPix2Pix - Edit Images With Prompts](https:\u002F\u002Flearnopencv.com\u002Finstructpix2pix\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FInstructPix2Pix-Edit-Images-With-Prompts) |\n| [NVIDIA Spring GTC 2023 Day 4: Ending on a High Note with Top Moments from the Finale!](https:\u002F\u002Flearnopencv.com\u002Fnvidia-spring-gtc-2023-day-4\u002F) | |\n| [NVIDIA Spring GTC 2023 Day 3: Digging deeper into Deep Learning, Semiconductors & more!](https:\u002F\u002Flearnopencv.com\u002Fnvidia-spring-gtc-2023-day-3-digging-deeper-into-deep-learning-semiconductors-more\u002F) | |\n| [NVIDIA Spring GTC 2023 Day 2: Jensen’s keynote & the iPhone moment of AI is here!](https:\u002F\u002Flearnopencv.com\u002Fnvidia-spring-gtc-2023-day-2-jensens-keynote-the-iphone-moment-of-ai-is-here\u002F) | |\n| [NVIDIA Spring GTC 2023 Day 1: Welcome to the future!](https:\u002F\u002Flearnopencv.com\u002Fnvidia-spring-gtc-2023-day-1-highlights-welcome-to-the-future\u002F) | |\n| [NVIDIA GTC Spring 2023 Curtain Raiser](https:\u002F\u002Flearnopencv.com\u002Fnvidia-gtc-spring-2023-curtain-raiser\u002F) | |\n| [Stable Diffusion - A New Paradigm in Generative AI](https:\u002F\u002Flearnopencv.com\u002Fstable-diffusion-generative-ai\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FStable-Diffusion-A-New-Paradigm-in-Generative-AI) |\n| [OpenCV Face Recognition – Does Face Recognition Work on AI-Generated Images?](https:\u002F\u002Flearnopencv.com\u002Fopencv-face-recognition-api\u002F) | |\n|[An In-Depth Guide to Denoising Diffusion Probabilistic Models – From Theory to Implementation](https:\u002F\u002Flearnopencv.com\u002Fdenoising-diffusion-probabilistic-models\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FGuide-to-training-DDPMs-from-Scratch)|\n|[From Pixels to Paintings: The Rise of Midjourney AI Art](https:\u002F\u002Flearnopencv.com\u002Frise-of-midjourney-ai-art\u002F)| |\n|[Mastering DALL·E 2: A Breakthrough in AI Art Generation](https:\u002F\u002Flearnopencv.com\u002Fmastering-dall-e-2\u002F)| |\n|[Top 10 AI Art Generation Tools using Diffusion Models](https:\u002F\u002Flearnopencv.com\u002Fai-art-generation-tools\u002F)| |\n|[The Future of Image Recognition is Here: PyTorch Vision Transformer](https:\u002F\u002Flearnopencv.com\u002Fthe-future-of-image-recognition-is-here-pytorch-vision-transformer\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVision_Transformer_PyTorch)|\n|[Understanding Attention Mechanism in Transformer Neural Networks](https:\u002F\u002Flearnopencv.com\u002Fattention-mechanism-in-transformer-neural-networks\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAttention_Mechanism_Introduction)|\n| [Deploying a Deep Learning Model using Hugging Face Spaces and Gradio](https:\u002F\u002Flearnopencv.com\u002Fdeploy-deep-learning-model-huggingface-spaces\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDeploying-a-Deep-Learning-Model-using-Hugging-Face-Spaces-and-Gradio) |\n| [Train YOLOv8 on Custom Dataset – A Complete Tutorial](https:\u002F\u002Flearnopencv.com\u002Ftrain-yolov8-on-custom-dataset\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTrain-YOLOv8-on-Custom-Dataset-A-Complete-Tutorial) |\n| [Introduction to Diffusion Models for Image Generation](https:\u002F\u002Flearnopencv.com\u002Fimage-generation-using-diffusion-models\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntroduction-to-Diffusion-Models-for-Image-Generation) |\n| [Building An Automated Image Annotation Tool: PyOpenAnnotate](https:\u002F\u002Flearnopencv.com\u002Fbuilding-automated-image-annotation-tool-pyopenannotate\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FBuilding-An-Automated-Image-Annotation-Tool-PyOpenAnnotate\u002F) |\n| [Ultralytics YOLOv8: State-of-the-Art YOLO Models](https:\u002F\u002Flearnopencv.com\u002Fultralytics-yolov8\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FUltralytics-YOLOv8-State-of-the-Art-YOLO-Models) |\n| [Getting Started with YOLOv5 Instance Segmentation](https:\u002F\u002Flearnopencv.com\u002Fgetting-started-with-yolov5-instance-segmentation\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FGetting-Started-with-YOLOv5-Instance-Segmentation) |\n|[The Ultimate Guide To DeepLabv3 - With PyTorch Inference](https:\u002F\u002Flearnopencv.com\u002Fdeeplabv3-ultimate-guide\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FThe-ultimate-guide-to-deeplabv3)|\n|[AI Fitness Trainer using MediaPipe: Squats Analysis](https:\u002F\u002Flearnopencv.com\u002Fai-fitness-trainer-using-mediapipe\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAI-Fitness-Trainer-Using-MediaPipe-Analyzing-Squats)|\n|[YoloR - Paper Explanation & Inference -An In-Depth Analysis](https:\u002F\u002Flearnopencv.com\u002Fyolor-paper-explanation-inference-an-in-depth-analysis\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYoloR-paper-explanation-analysis)|\n|[Roadmap To an Automated Image Annotation Tool Using Python](https:\u002F\u002Flearnopencv.com\u002Fautomated-image-annotation-tool-using-opencv-python\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FRoadmap-To-an-Automated-Image-Annotation-Tool-Using-Python)|\n|[Performance Comparison of YOLO Object Detection Models – An Intensive Study](https:\u002F\u002Flearnopencv.com\u002Fperformance-comparison-of-yolo-models\u002F)||\n|[FCOS - Anchor Free Object Detection Explained](https:\u002F\u002Flearnopencv.com\u002Ffcos-anchor-free-object-detection-explained\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFCOS-Inference-using-PyTorch)|\n| [YOLOv6 Custom Dataset Training – Underwater Trash Detection](https:\u002F\u002Flearnopencv.com\u002Fyolov6-custom-dataset-training\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv6-Custom-Dataset-Training-Underwater-Trash-Detection) |\n|[What is EXIF Data in Images?](https:\u002F\u002Fwww.learnopencv.com\u002Fwhat-is-exif-data-in-images\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FWhat-is-EXIF-Data-in-Images)|\n|[t-SNE: T-Distributed Stochastic Neighbor Embedding Explained](https:\u002F\u002Flearnopencv.com\u002Ft-sne-t-distributed-stochastic-neighbor-embedding-explained\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Ft-SNE-with-Tensorboard)|\n|[CenterNet: Objects as Points – Anchor-free Object Detection Explained](https:\u002F\u002Flearnopencv.com\u002Fcenternet-anchor-free-object-detection-explained\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fcenternet-with-tf-hub)|\n|[YOLOv7 Pose vs MediaPipe in Human Pose Estimation](https:\u002F\u002Flearnopencv.com\u002Fyolov7-pose-vs-mediapipe-in-human-pose-estimation\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv7-Pose-vs-MediaPipe-in-Human-Pose-Estimation)|\n|[YOLOv6 Object Detection – Paper Explanation and Inference](https:\u002F\u002Flearnopencv.com\u002Fyolov6-object-detection\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv6-Object-Detection-Paper-Explanation-and-Inference)|\n|[YOLOX Object Detector Paper Explanation and Custom Training](https:\u002F\u002Flearnopencv.com\u002Fyolox-object-detector-paper-explanation-and-custom-training\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOX-Object-Detection-Paper-Explanation-and-Custom-Training)|\n|[Driver Drowsiness Detection Using Mediapipe In Python](https:\u002F\u002Flearnopencv.com\u002Fdriver-drowsiness-detection-using-mediapipe-in-python\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDriver-Drowsiness-detection-using-Mediapipe-in-Python)|\n|[GTC 2022 Big Bang AI announcements: Everything you need to know](https:\u002F\u002Flearnopencv.com\u002Fgtc-2022-big-bang-ai-announcements-everything-you-need-to-know\u002F)||\n|[NVIDIA GTC 2022 : The most important AI event this Fall](https:\u002F\u002Flearnopencv.com\u002Fnvidia-gtc-2022-the-most-important-ai-event-this-fall\u002F)||\n|[Object Tracking and Reidentification with FairMOT](https:\u002F\u002Flearnopencv.com\u002Fobject-tracking-and-reidentification-with-fairmot\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FObject-Tracking-and-Reidentification-with-FairMOT) |\n|[What is Face Detection? – The Ultimate Guide for 2022](https:\u002F\u002Flearnopencv.com\u002Fwhat-is-face-detection-the-ultimate-guide\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFace-Detection-Ultimate-Guide) |\n|[Document Scanner: Custom Semantic Segmentation using PyTorch-DeepLabV3](https:\u002F\u002Flearnopencv.com\u002Fcustom-document-segmentation-using-deep-learning\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDocument-Scanner-Custom-Semantic-Segmentation-using-PyTorch-DeepLabV3)|\n|[Fine Tuning YOLOv7 on Custom Dataset](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-yolov7-on-custom-dataset\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-YOLOv7)|\n|[Center Stage for Zoom Calls using MediaPipe](https:\u002F\u002Flearnopencv.com\u002FCenter-Stage-for-zoom-call-using-mediapipe\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FCenterStage)|\n|[Mean Average Precision (mAP) in Object Detection](https:\u002F\u002Flearnopencv.com\u002Fmean-average-precision-map-object-detection-model-evaluation-metric\u002F)||\n|[YOLOv7 Object Detection Paper Explanation and Inference](https:\u002F\u002Flearnopencv.com\u002Fyolov7-object-detection-paper-explanation-and-inference\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv7-Object-Detection-Paper-Explanation-and-Inference)|\n|[Pothole Detection using YOLOv4 and Darknet](https:\u002F\u002Flearnopencv.com\u002Fpothole-detection-using-yolov4-and-darknet\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPothole-Detection-using-YOLOv4-and-Darknet)|\n|[Automatic Document Scanner using OpenCV](https:\u002F\u002Flearnopencv.com\u002Fautomatic-document-scanner-using-opencv\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAutomatic-Document-Scanner)|\n|[Demystifying GPU architectures for deep learning: Part 2](https:\u002F\u002Flearnopencv.com\u002Fdemystifying-gpu-architectures-for-deep-learning-part-2\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fgpu_arch_and_CUDA)|\n|[Demystifying GPU Architectures For Deep Learning](https:\u002F\u002Flearnopencv.com\u002Fdemystifying-gpu-architectures-for-deep-learning\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fgpu_arch_and_CUDA)|\n|[Intersection-over-Union(IoU)-in-Object-Detection-and-Segmentation](https:\u002F\u002Flearnopencv.com\u002Fintersection-over-unioniou-in-object-detection-and-segmentation\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntersection-over-Union-IoU-in-Object-Detection-and-Segmentation)|\n|[Understanding Multiple Object Tracking using DeepSORT](https:\u002F\u002Flearnopencv.com\u002Funderstanding-multiple-object-tracking-using-deepsort\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FUnderstanding-Multiple-Object-Tracking-using-DeepSORT)|\n|[Optical Character Recognition using PaddleOCR](https:\u002F\u002Flearnopencv.com\u002Foptical-character-recognition-using-paddleocr\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOptical-Character-Recognition-using-PaddleOCR)|\n|[Gesture Control in Zoom Call using Mediapipe](https:\u002F\u002Flearnopencv.com\u002Fgesture-control-in-zoom-call-using-mediapipe\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fzoom-gestures)|\n|[A Deep Dive into Tensorflow Model Optimization](https:\u002F\u002Flearnopencv.com\u002Fdeep-dive-into-tensorflow-model-optimization-toolkit\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FA-Deep-Dive-into-Tensorflow-Model-Optimization)|\n|[DepthAI Pipeline Overview: Creating a Complex Pipeline](https:\u002F\u002Flearnopencv.com\u002Fdepthai-pipeline-overview-creating-a-complex-pipeline\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOAK-DepthAi-Pipeline-Overview)|\n|[TensorFlow Lite Model Maker: Create Models for On-Device Machine Learning](https:\u002F\u002Flearnopencv.com\u002Ftensorflow-lite-model-maker-create-models-for-on-device-machine-learning\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTensorflow-Lite-Model-Maker-Create-Models-for-On-Device-ML)|\n|[TensorFlow Lite: Model Optimization for On Device Machine Learning](https:\u002F\u002Flearnopencv.com\u002Ftensorflow-lite-model-optimization-for-on-device-machine-learning)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTensorFlow-Lite-Model-Optimization-for-On-Device-MachineLearning)|\n|[Object detection with depth measurement using pre-trained models with OAK-D](https:\u002F\u002Flearnopencv.com\u002Fobject-detection-with-depth-measurement-with-oak-d\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOAK-Object-Detection-with-Depth)|\n|[Custom Object Detection Training using YOLOv5](https:\u002F\u002Flearnopencv.com\u002Fcustom-object-detection-training-using-yolov5\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FCustom-Object-Detection-Training-using-YOLOv5)|\n|[Object Detection using Yolov5 and OpenCV DNN (C++\u002FPython)](https:\u002F\u002Flearnopencv.com\u002Fobject-detection-using-yolov5-and-opencv-dnn-in-c-and-python\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FObject-Detection-using-YOLOv5-and-OpenCV-DNN-in-CPP-and-Python)|\n|[Create Snapchat\u002FInstagram filters using Mediapipe](https:\u002F\u002Flearnopencv.com\u002Fcreate-snapchat-instagram-filters-using-mediapipe\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FCreate-AR-filters-using-Mediapipe)|\n|[AUTOSAR C++ compliant deep learning inference with TensorRT](https:\u002F\u002Flearnopencv.com\u002Fautosar-c-compliant-deep-learning-inference-with-tensorrt\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Findustrial_cv_TensorRT_cpp)|\n|[NVIDIA GTC 2022 Day 4 Highlights: Meet the new Jetson Orin](https:\u002F\u002Flearnopencv.com\u002Fnvidia-gtc-2022-day-4-highlights-meet-the-new-jetson-orin\u002F)||\n|[NVIDIA GTC 2022 Day 3 Highlights: Deep Dive into Hopper architecture](https:\u002F\u002Flearnopencv.com\u002Fnvidia-gtc-2022-day-3-highlights-deep-dive-into-hopper-architecture\u002F)||\n|[NVIDIA GTC 2022 Day 2 Highlights: Jensen’s Keynote](https:\u002F\u002Flearnopencv.com\u002Fnvidia-gtc-2022-day-2-highlights\u002F)||\n|[NVIDIA GTC 2022 Day 1 Highlights: Brilliant Start](https:\u002F\u002Flearnopencv.com\u002Fgtc-day-1-highlights\u002F)||\n|[Automatic License Plate Recognition using Python](https:\u002F\u002Flearnopencv.com\u002Fautomatic-license-plate-recognition-using-deep-learning\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FALPR)|\n|[Building a Poor Body Posture Detection and Alert System using MediaPipe](https:\u002F\u002Flearnopencv.com\u002Fbuilding-a-body-posture-analysis-system-using-mediapipe\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPosture-analysis-system-using-MediaPipe-Pose)|\n|[Introduction to MediaPipe](https:\u002F\u002Flearnopencv.com\u002Fintroduction-to-mediapipe\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntroduction-to-MediaPipe)|\n|[Disparity Estimation using Deep Learning](https:\u002F\u002Flearnopencv.com\u002Fdisparity-estimation-using-deep-learning\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDisparity-Estimation-Using-Deep-Learning)|\n|[How to build Chrome Dino game bot using OpenCV Feature Matching](https:\u002F\u002Flearnopencv.com\u002Fhow-to-build-chrome-dino-game-bot-using-opencv-feature-matching\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FChrome-Dino-Bot-using-OpenCV-feature-matching)|\n|[Top 10 Sources to Find Computer Vision and AI Models](https:\u002F\u002Flearnopencv.com\u002Ftop-10-sources-to-find-computer-vision-and-ai-models\u002F)||\n|[Multi-Attribute and Graph-based Object Detection](https:\u002F\u002Flearnopencv.com\u002Fmulti-attribute-and-graph-based-object-detection\u002F)||\n|[Plastic Waste Detection with Deep Learning](https:\u002F\u002Flearnopencv.com\u002Fplastic-waste-detection-with-deep-learning\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPlastic-Waste-Detection-with-Deep-Learning)|\n|[Ensemble Deep Learning-based Defect Classification and Detection in SEM Images](https:\u002F\u002Flearnopencv.com\u002Fensemble-deep-learning-based-defect-classification-and-detection-in-sem-images\u002F)||\n|[Building Industrial embedded deep learning inference pipelines with TensorRT](https:\u002F\u002Flearnopencv.com\u002Fbuilding-industrial-embedded-deep-learning-inference-pipelines-with-tensorrt\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Findustrial_cv_TensorRT_python)|\n|[Transfer Learning for Medical Images](https:\u002F\u002Flearnopencv.com\u002Ftransfer-learning-for-medical-images\u002F)||\n|[Stereo Vision and Depth Estimation using OpenCV AI Kit](https:\u002F\u002Flearnopencv.com\u002Fstereo-vision-and-depth-estimation-using-opencv-ai-kit\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Foak-getting-started)|\n|[Introduction to OpenCV AI Kit and DepthAI](https:\u002F\u002Flearnopencv.com\u002Fintroduction-to-opencv-ai-kit-and-depthai\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Foak-getting-started)|\n|[WeChat QR Code Scanner in OpenCV](https:\u002F\u002Flearnopencv.com\u002Fwechat-qr-code-scanner-in-opencv)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FWeChat-QRCode-Scanner-OpenCV)|\n|[AI behind the Diwali 2021 ‘Not just a Cadbury ad’](https:\u002F\u002Flearnopencv.com\u002Fai-behind-the-diwali-2021-not-just-a-cadbury-ad\u002F)| |\n|[Model Selection and Benchmarking with Modelplace.AI](https:\u002F\u002Flearnopencv.com\u002Fmodel-selection-and-benchmarking-with-modelplace-ai\u002F)|[Model Zoo](https:\u002F\u002Fmodelplace.ai\u002F)|\n|[Real-time style transfer in a zoom meeting](https:\u002F\u002Flearnopencv.com\u002Freal-time-style-transfer-in-a-zoom-meeting\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fstyle-transfer-zoom)|\n| [Introduction to OpenVino Deep Learning Workbench](https:\u002F\u002Flearnopencv.com\u002Fintroduction-to-openvino-deep-learning-workbench\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntroduction-to-OpenVino-Deep-Learning-Workbench) |\n| [Running OpenVino Models on Intel Integrated GPU](https:\u002F\u002Flearnopencv.com\u002Frunning-openvino-models-on-intel-integrated-gpu\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FRunning-OpenVino-Models-on-Intel-Integrated-GPU) |\n|[Post Training Quantization with OpenVino Toolkit](https:\u002F\u002Flearnopencv.com\u002Fpost-training-quantization-with-openvino-toolkit\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPost-Training-Quantization-with-OpenVino-Toolkit)|\n|[Introduction to Intel OpenVINO Toolkit](https:\u002F\u002Flearnopencv.com\u002Fintroduction-to-intel-openvino-toolkit\u002F)||\n|[Human Action Recognition using Detectron2 and LSTM](https:\u002F\u002Flearnopencv.com\u002Fhuman-action-recognition-using-detectron2-and-lstm\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHuman-Action-Recognition-Using-Detectron2-And-Lstm)|\n|[Pix2Pix:Image-to-Image Translation in PyTorch & TensorFlow](https:\u002F\u002Flearnopencv.com\u002Fpaired-image-to-image-translation-pix2pix\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImage-to-Image-Translation-with-GAN)|\n|[Conditional GAN (cGAN) in PyTorch and TensorFlow](https:\u002F\u002Flearnopencv.com\u002Fconditional-gan-cgan-in-pytorch-and-tensorflow\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FConditional-GAN-PyTorch-TensorFlow)|\n|[Deep Convolutional GAN in PyTorch and TensorFlow](https:\u002F\u002Flearnopencv.com\u002Fdeep-convolutional-gan-in-pytorch-and-tensorflow\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDeep-Convolutional-GAN)|\n|[Introduction to Generative Adversarial Networks (GANs)](https:\u002F\u002Flearnopencv.com\u002Fintroduction-to-generative-adversarial-networks\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntro-to-Generative-Adversarial-Network)|\n|[Human Pose Estimation using Keypoint RCNN in PyTorch](https:\u002F\u002Flearnopencv.com\u002Fhuman-pose-estimation-using-keypoint-rcnn-in-pytorch\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-Keypoint-RCNN)|\n|[Non Maximum Suppression: Theory and Implementation in PyTorch](https:\u002F\u002Flearnopencv.com\u002Fnon-maximum-suppression-theory-and-implementation-in-pytorch)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FNon-Maximum-Suppression)|\n|[MRNet – The Multi-Task Approach](https:\u002F\u002Flearnopencv.com\u002Fmrnet-multitask-approach\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMRnet-MultiTask-Approach) |\n|[Generative and Discriminative Models](https:\u002F\u002Flearnopencv.com\u002Fgenerative-and-discriminative-models\u002F)| |\n|[Playing Chrome's T-Rex Game with Facial Gestures](https:\u002F\u002Flearnopencv.com\u002Fplaying-chromes-t-rex-game-with-facial-gestures\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPlaying-Chrome-TRex-Game-with-Facial-Gestures) |\n|[Variational Autoencoder in TensorFlow](https:\u002F\u002Flearnopencv.com\u002Fvariational-autoencoder-in-tensorflow\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVariational-Autoencoder-TensorFlow) |\n|[Autoencoder in TensorFlow 2: Beginner’s Guide](https:\u002F\u002Flearnopencv.com\u002Fautoencoder-in-tensorflow-2-beginners-guide\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAutoencoder-in-TensorFlow) |\n|[Deep Learning with OpenCV DNN Module: A Definitive Guide](https:\u002F\u002Flearnopencv.com\u002Fdeep-learning-with-opencvs-dnn-module-a-definitive-guide\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDeep-Learning-with-OpenCV-DNN-Module) |\n|[Depth perception using stereo camera (Python\u002FC++)](https:\u002F\u002Flearnopencv.com\u002Fdepth-perception-using-stereo-camera-python-c\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDepth-Perception-Using-Stereo-Camera) |\n|[Contour Detection using OpenCV (Python\u002FC++)](https:\u002F\u002Flearnopencv.com\u002Fcontour-detection-using-opencv-python-c\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FContour-Detection-using-OpenCV) |\n|[Super Resolution in OpenCV](https:\u002F\u002Flearnopencv.com\u002Fsuper-resolution-in-opencv\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FSuper-Resolution-in-OpenCV) |\n|[Improving Illumination in Night Time Images](https:\u002F\u002Flearnopencv.com\u002Fimproving-illumination-in-night-time-images\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImproving-Illumination-in-Night-Time-Images) |\n|[Video Classification and Human Activity Recognition](https:\u002F\u002Flearnopencv.com\u002Fintroduction-to-video-classification-and-human-activity-recognition\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fvideo-classification-and-human-activity-recognition) |\n|[How to use OpenCV DNN Module with Nvidia GPU on Windows](https:\u002F\u002Flearnopencv.com\u002Fhow-to-use-opencv-dnn-module-with-nvidia-gpu-on-windows) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOpenCV-dnn-gpu-support-Windows) |\n|[How to use OpenCV DNN Module with NVIDIA GPUs](https:\u002F\u002Flearnopencv.com\u002Fopencv-dnn-with-gpu-support\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOpenCV-dnn-gpu-support-Linux) |\n|[Code OpenCV in Visual Studio](https:\u002F\u002Flearnopencv.com\u002Fcode-opencv-in-visual-studio\u002F) | |\n|[Install OpenCV on Windows – C++ \u002F Python](https:\u002F\u002Flearnopencv.com\u002Finstall-opencv-on-windows\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FInstall-OpenCV-Windows-exe) |\n|[Face Recognition with ArcFace](https:\u002F\u002Fwww.learnopencv.com\u002Fface-recognition-with-arcface\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFace-Recognition-with-ArcFace)|\n|[Background Subtraction with OpenCV and BGS Libraries](https:\u002F\u002Fwww.learnopencv.com\u002Fbackground-subtraction-with-opencv-and-bgs-libraries\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FBackground-Subtraction) |\n|[RAFT: Optical Flow estimation using Deep Learning](https:\u002F\u002Flearnopencv.com\u002Foptical-flow-using-deep-learning-raft\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOptical-Flow-Estimation-using-Deep-Learning-RAFT)|\n|[Making A Low-Cost Stereo Camera Using OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fmaking-a-low-cost-stereo-camera-using-opencv\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fstereo-camera)|\n|[Optical Flow in OpenCV (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Foptical-flow-in-opencv)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOptical-Flow-in-OpenCV)|\n|[Introduction to Epipolar Geometry and Stereo Vision](https:\u002F\u002Fwww.learnopencv.com\u002Fintroduction-to-epipolar-geometry-and-stereo-vision\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FEpipolarGeometryAndStereoVision)|\n|[Classification With Localization: Convert any keras Classifier to a Detector](https:\u002F\u002Fwww.learnopencv.com\u002Fclassification-with-localization\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FClassification-with-localization-convert-any-keras-classifier-into-a-detector\u002FREADME.md) |\n|[Photoshop Filters in OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fphotoshop-filters-in-opencv\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPhotoshop-Filters-in-OpenCV)|\n|[Tetris Game using OpenCV Python](https:\u002F\u002Fwww.learnopencv.com\u002Ftetris-with-opencv-python)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTetris)|\n|[Image Classification with OpenCV for Android](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-classification-with-opencv-for-android\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDNN-OpenCV-Classification-Android) |\n|[Image Classification with OpenCV Java](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-classification-with-opencv-java)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDNN-OpenCV-Classification-with-Java) |\n|[PyTorch to Tensorflow Model Conversion](https:\u002F\u002Fwww.learnopencv.com\u002Fpytorch-to-tensorflow-model-conversion\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-to-TensorFlow-Model-Conversion) |\n|[Snake Game with OpenCV Python](https:\u002F\u002Fwww.learnopencv.com\u002Fsnake-game-with-opencv-python\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSnakeGame) |\n|[Stanford MRNet Challenge: Classifying Knee MRIs](https:\u002F\u002Fwww.learnopencv.com\u002Fstanford-mrnet-challenge-classifying-knee-mris\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMRNet-Single-Model) |\n|[Experiment Logging with TensorBoard and wandb](https:\u002F\u002Fwww.learnopencv.com\u002Fexperiment-logging-with-tensorboard-and-wandb)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-Vision-Experiment-Logging) |\n|[Understanding Lens Distortion](https:\u002F\u002Fwww.learnopencv.com\u002Funderstanding-lens-distortion\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FUnderstandingLensDistortion) |\n|[Image Matting with state-of-the-art Method “F, B, Alpha Matting”](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-matting-with-state-of-the-art-method-f-b-alpha-matting\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFBAMatting) |\n|[Bag Of Tricks For Image Classification - Let's check if it is working or not](https:\u002F\u002Fwww.learnopencv.com\u002Fbag-of-tricks-for-image-classification-lets-check-if-it-is-working-or-not\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FBag-Of-Tricks-For-Image-Classification) |\n|[Getting Started with OpenCV CUDA Module](https:\u002F\u002Fwww.learnopencv.com\u002Fgetting-started-opencv-cuda-module\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FGetting-Started-OpenCV-CUDA-Module) |\n|[Training a Custom Object Detector with DLIB & Making Gesture Controlled Applications](https:\u002F\u002Fwww.learnopencv.com\u002Ftraining-a-custom-object-detector-with-dlib-making-gesture-controlled-applications\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTraining_a_custom_hand_detector_with_dlib) |\n|[How To Run Inference Using TensorRT C++ API](https:\u002F\u002Fwww.learnopencv.com\u002Fhow-to-run-inference-using-tensorrt-c-api\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-ONNX-TensorRT-CPP) |\n|[Using Facial Landmarks for Overlaying Faces with Medical Masks](https:\u002F\u002Fwww.learnopencv.com\u002Fusing-facial-landmarks-for-overlaying-faces-with-masks\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFaceMaskOverlay) |\n|[Tensorboard with PyTorch Lightning](https:\u002F\u002Fwww.learnopencv.com\u002Ftensorboard-with-pytorch-lightning)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTensorBoard-With-Pytorch-Lightning) |\n|[Otsu's Thresholding with OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fotsu-thresholding-with-opencv\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fotsu-method) |\n|[PyTorch-to-CoreML-model-conversion](https:\u002F\u002Fwww.learnopencv.com\u002Fpytorch-to-coreml-model-conversion\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-to-CoreML-model-conversion) |\n|[Playing Rock, Paper, Scissors with AI](https:\u002F\u002Fwww.learnopencv.com\u002Fplaying-rock-paper-scissors-with-ai\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPlaying-rock-paper-scissors-with-AI) |\n|[CNN Receptive Field Computation Using Backprop with TensorFlow](https:\u002F\u002Fwww.learnopencv.com\u002Fcnn-receptive-field-computation-using-backprop-with-tensorflow\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTensorFlow-Receptive-Field-With-Backprop)|\n|[CNN Fully Convolutional Image Classification with TensorFlow](https:\u002F\u002Fwww.learnopencv.com\u002Fcnn-fully-convolutional-image-classification-with-tensorflow) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTensorFlow-Fully-Convolutional-Image-Classification) |\n|[How to convert a model from PyTorch to TensorRT and speed up inference](https:\u002F\u002Fwww.learnopencv.com\u002Fhow-to-convert-a-model-from-pytorch-to-tensorrt-and-speed-up-inference\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-ONNX-TensorRT) |\n|[Efficient image loading](https:\u002F\u002Fwww.learnopencv.com\u002Fefficient-image-loading\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FEfficient-image-loading) |\n|[Graph Convolutional Networks: Model Relations In Data](https:\u002F\u002Fwww.learnopencv.com\u002Fgraph-convolutional-networks-model-relations-in-data\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FGraph-Convolutional-Networks-Model-Relations-In-Data)|\n|[Getting Started with Federated Learning with PyTorch and PySyft](https:\u002F\u002Fwww.learnopencv.com\u002Ffederated-learning-using-pytorch-and-pysyft\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFederated-Learning-Intro)|\n|[Creating a Virtual Pen & Eraser](http:\u002F\u002Fwww.learnopencv.com\u002Fcreating-a-virtual-pen-and-eraser-with-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FCreating-a-Virtual-Pen-and-Eraser) |\n|[Getting Started with PyTorch Lightning](https:\u002F\u002Fwww.learnopencv.com\u002Fgetting-started-with-pytorch-lightning\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPytorch-Lightning)|\n|[Multi-Label Image Classification with PyTorch: Image Tagging](https:\u002F\u002Fwww.learnopencv.com\u002Fmulti-label-image-classification-with-pytorch-image-tagging\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-Multi-Label-Image-Classification-Image-Tagging)|\n|[Funny Mirrors Using OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002FFunny-Mirrors-Using-OpenCV\u002F)|[code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFunnyMirrors)|\n|[t-SNE for ResNet feature visualization](https:\u002F\u002Fwww.learnopencv.com\u002Ft-sne-for-feature-visualization\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTSNE)|\n|[Multi-Label Image Classification with Pytorch](https:\u002F\u002Fwww.learnopencv.com\u002Fmulti-label-image-classification-with-pytorch\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-Multi-Label-Image-Classification)|\n|[CNN Receptive Field Computation Using Backprop](https:\u002F\u002Fwww.learnopencv.com\u002Fcnn-receptive-field-computation-using-backprop\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-Receptive-Field-With-Backprop)|\n|[CNN Receptive Field Computation Using Backprop with TensorFlow](https:\u002F\u002Fwww.learnopencv.com\u002Fcnn-receptive-field-computation-using-backprop-with-tensorflow\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTensorFlow-Receptive-Field-With-Backprop)|\n|[Augmented Reality using AruCo Markers in OpenCV(C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Faugmented-reality-using-aruco-markers-in-opencv-(c++-python)\u002F) |[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAugmentedRealityWithArucoMarkers)|\n|[Fully Convolutional Image Classification on Arbitrary Sized Image](https:\u002F\u002Fwww.learnopencv.com\u002Ffully-convolutional-image-classification-on-arbitrary-sized-image\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-Fully-Convolutional-Image-Classification)|\n|[Camera Calibration using OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fcamera-calibration-using-opencv\u002F) |[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FCameraCalibration)|\n|[Geometry of Image Formation](https:\u002F\u002Fwww.learnopencv.com\u002Fgeometry-of-image-formation\u002F) ||\n|[Ensuring Training Reproducibility in Pytorch](https:\u002F\u002Fwww.learnopencv.com\u002Fensuring-training-reproducibility-in-pytorch) ||\n|[Gaze Tracking](https:\u002F\u002Fwww.learnopencv.com\u002Fgaze-tracking\u002F) ||\n|[Simple Background Estimation in Videos Using OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fsimple-background-estimation-in-videos-using-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVideoBackgroundEstimation)|\n|[Applications of Foreground-Background separation with Semantic Segmentation](https:\u002F\u002Fwww.learnopencv.com\u002Fapplications-of-foreground-background-separation-with-semantic-segmentation\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fapp-seperation-semseg) |\n|[EfficientNet: Theory + Code](https:\u002F\u002Fwww.learnopencv.com\u002Fefficientnet-theory-code) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FEfficientNet) |\n|[PyTorch for Beginners: Mask R-CNN Instance Segmentation with PyTorch](https:\u002F\u002Fwww.learnopencv.com\u002Fmask-r-cnn-instance-segmentation-with-pytorch\u002F) | [Code](.\u002FPyTorch-Mask-RCNN) |\n|[PyTorch for Beginners: Faster R-CNN Object Detection with PyTorch](https:\u002F\u002Fwww.learnopencv.com\u002Ffaster-r-cnn-object-detection-with-pytorch) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-faster-RCNN) |\n|[PyTorch for Beginners: Semantic Segmentation using torchvision](https:\u002F\u002Fwww.learnopencv.com\u002Fpytorch-for-beginners-semantic-segmentation-using-torchvision\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-Segmentation-torchvision) |\n|[PyTorch for Beginners: Comparison of pre-trained models for Image Classification](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-classification-using-pre-trained-models-using-pytorch\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImage-classification-pre-trained-models\u002FImage_Classification_using_pre_trained_models.ipynb) |\n|[PyTorch for Beginners: Basics](https:\u002F\u002Fwww.learnopencv.com\u002Fpytorch-for-beginners-basics\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-for-Beginners\u002FPyTorch_for_Beginners.ipynb) |\n|[PyTorch Model Inference using ONNX and Caffe2](https:\u002F\u002Fwww.learnopencv.com\u002Fpytorch-model-inference-using-onnx-and-caffe2\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FInference-for-PyTorch-Models\u002FONNX-Caffe2) |\n|[Image Classification Using Transfer Learning in PyTorch](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-classification-using-transfer-learning-in-pytorch\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImage-Classification-in-PyTorch) |\n|[Hangman: Creating games in OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fhangman-creating-games-in-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHangman) |\n|[Image Inpainting with OpenCV (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-inpainting-with-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImage-Inpainting) |\n|[Hough Transform with OpenCV (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Fhough-transform-with-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHough-Transform) |\n|[Xeus-Cling: Run C++ code in Jupyter Notebook](https:\u002F\u002Fwww.learnopencv.com\u002Fxeus-cling-run-c-code-in-jupyter-notebook\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FXeusCling) |\n|[Gender & Age Classification using OpenCV Deep Learning ( C++\u002FPython )](https:\u002F\u002Fwww.learnopencv.com\u002Fage-gender-classification-using-opencv-deep-learning-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAgeGender) |\n|[Invisibility Cloak using Color Detection and Segmentation with OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Finvisibility-cloak-using-color-detection-and-segmentation-with-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FInvisibilityCloak) |\n|[Fast Image Downloader for Open Images V4 (Python)](https:\u002F\u002Fwww.learnopencv.com\u002Ffast-image-downloader-for-open-images-v4\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FdownloadOpenImages) |\n|[Deep Learning based Text Detection Using OpenCV (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-based-text-detection-using-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTextDetectionEAST) |\n|[Video Stabilization Using Point Feature Matching in OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fvideo-stabilization-using-point-feature-matching-in-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVideoStabilization) |\n|[Training YOLOv3 : Deep Learning based Custom Object Detector](https:\u002F\u002Fwww.learnopencv.com\u002Ftraining-yolov3-deep-learning-based-custom-object-detector\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv3-Training-Snowman-Detector ) |\n|[Using OpenVINO with OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fusing-openvino-with-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOpenVINO-OpenCV) |\n|[Duplicate Search on Quora Dataset](https:\u002F\u002Fwww.learnopencv.com\u002Fduplicate-search-on-quora-dataset\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FQuora-Dataset-Duplicate-Search) |\n|[Shape Matching using Hu Moments (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Fshape-matching-using-hu-moments-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHuMoments) |\n|[Install OpenCV 4 on CentOS (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-4-on-centos-7\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-3-on-centos.sh) |\n|[Install OpenCV 3.4.4 on CentOS (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-3-4-4-on-centos-7\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-3-on-centos.sh) |\n|[Install OpenCV 3.4.4 on Red Hat (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-3-4-4-on-red-hat\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-3-on-red-hat.sh) |\n|[Install OpenCV 4 on Red Hat (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-4-on-red-hat\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-4-on-red-hat.sh) |\n|[Install OpenCV 4 on macOS (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-4-on-macos\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-4-macos.sh) |\n|[Install OpenCV 3.4.4 on Raspberry Pi](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-3-4-4-on-raspberry-pi\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-3-raspberry-pi.sh) |\n|[Install OpenCV 3.4.4 on macOS (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-3-4-4-on-macos\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-3-macos.sh) |\n|[OpenCV QR Code Scanner (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Fopencv-qr-code-scanner-c-and-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FQRCode-OpenCV) |\n|[Install OpenCV 3.4.4 on Windows (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-3-4-4-on-windows\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FInstallScripts\u002FWindows-3) |\n|[Install OpenCV 3.4.4 on Ubuntu 16.04 (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-3-4-4-on-ubuntu-16-04\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-3-on-Ubuntu-16-04.sh) |\n|[Install OpenCV 3.4.4 on Ubuntu 18.04 (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-3-4-4-on-ubuntu-18-04\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-3-on-Ubuntu-18-04.sh) |\n|[Universal Sentence Encoder](https:\u002F\u002Fwww.learnopencv.com\u002Funiversal-sentence-encoder) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FUniversal-Sentence-Encoder) |\n|[Install OpenCV 4 on Raspberry Pi](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-4-on-raspberry-pi\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-4-raspberry-pi.sh) |\n|[Install OpenCV 4 on Windows (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-4-on-windows\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FInstallScripts\u002FWindows-4) |\n|[Face Detection – Dlib, OpenCV, and Deep Learning ( C++ \u002F Python )](https:\u002F\u002Flearnopencv.com\u002Fface-detection-opencv-dlib-and-deep-learning-c-python\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFaceDetectionComparison)|\n|[Hand Keypoint Detection using Deep Learning and OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fhand-keypoint-detection-using-deep-learning-and-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHandPose)|\n|[Deep learning based Object Detection and Instance Segmentation using Mask R-CNN in OpenCV (Python \u002F C++)](https:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-based-object-detection-and-instance-segmentation-using-mask-r-cnn-in-opencv-python-c\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMask-RCNN) |\n|[Install OpenCV 4 on Ubuntu 18.04 (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-4-on-ubuntu-18-04\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-4-on-Ubuntu-18-04.sh) |\n|[Install OpenCV 4 on Ubuntu 16.04 (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-4-on-ubuntu-16-04\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-4-on-Ubuntu-16-04.sh) |\n|[Multi-Person Pose Estimation in OpenCV using OpenPose](https:\u002F\u002Fwww.learnopencv.com\u002Fmulti-person-pose-estimation-in-opencv-using-openpose\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOpenPose-Multi-Person) |\n|[Heatmap for Logo Detection using OpenCV (Python)](https:\u002F\u002Fwww.learnopencv.com\u002Fheatmap-for-logo-detection-using-opencv-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fheatmap)|\n|[Deep Learning based Object Detection using YOLOv3 with OpenCV ( Python \u002F C++ )](https:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-based-object-detection-using-yolov3-with-opencv-python-c\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FObjectDetection-YOLO)|\n|[Convex Hull using OpenCV in Python and C++](https:\u002F\u002Fwww.learnopencv.com\u002Fconvex-hull-using-opencv-in-python-and-c\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FConvexHull)|\n|[MultiTracker : Multiple Object Tracking using OpenCV (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Fmultitracker-multiple-object-tracking-using-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMultiObjectTracker) |\n|[Convolutional Neural Network based Image Colorization using OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fconvolutional-neural-network-based-image-colorization-using-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FColorization)|\n|[SVM using scikit-learn](https:\u002F\u002Fwww.learnopencv.com\u002Fsvm-using-scikit-learn-in-python\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSVM-using-Python)|\n|[GOTURN: Deep Learning based Object Tracking](https:\u002F\u002Fwww.learnopencv.com\u002Fgoturn-deep-learning-based-object-tracking\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FGOTURN)|\n|[Find the Center of a Blob (Centroid) using OpenCV (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Ffind-center-of-blob-centroid-using-opencv-cpp-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FCenterofBlob)|\n|[Support Vector Machines (SVM)](https:\u002F\u002Fwww.learnopencv.com\u002Fsupport-vector-machines-svm\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSVM-using-Python)|\n|[Batch Normalization in Deep Networks](https:\u002F\u002Fwww.learnopencv.com\u002Fbatch-normalization-in-deep-networks\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FBatchNormalization)|\n|[Deep Learning based Character Classification using Synthetic Dataset](https:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-character-classification-using-synthetic-dataset\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FCharClassification)|\n|[Image Quality Assessment : BRISQUE](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-quality-assessment-brisque\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImageMetrics)|\n|[Understanding AlexNet](https:\u002F\u002Fwww.learnopencv.com\u002Funderstanding-alexnet\u002F)||\n|[Deep Learning based Text Recognition (OCR) using Tesseract and OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-based-text-recognition-ocr-using-tesseract-and-opencv\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOCR)|\n|[Deep Learning based Human Pose Estimation using OpenCV ( C++ \u002F Python )](https:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-based-human-pose-estimation-using-opencv-cpp-python\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOpenPose)|\n|[Number of Parameters and Tensor Sizes in a Convolutional Neural Network (CNN)](https:\u002F\u002Fwww.learnopencv.com\u002Fnumber-of-parameters-and-tensor-sizes-in-convolutional-neural-network\u002F)| |\n|[How to convert your OpenCV C++ code into a Python module](https:\u002F\u002Fwww.learnopencv.com\u002Fhow-to-convert-your-opencv-c-code-into-a-python-module\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fpymodule)|\n|[CV4Faces : Best Project Award 2018](https:\u002F\u002Fwww.learnopencv.com\u002Fcv4faces-best-project-award-2018\u002F)| |\n|[Facemark : Facial Landmark Detection using OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Ffacemark-facial-landmark-detection-using-opencv\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFacialLandmarkDetection)|\n|[Image Alignment (Feature Based) using OpenCV (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-alignment-feature-based-using-opencv-c-python\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImageAlignment-FeatureBased)|\n|[Barcode and QR code Scanner using ZBar and OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fbarcode-and-qr-code-scanner-using-zbar-and-opencv\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fbarcode-QRcodeScanner)|\n|[Keras Tutorial : Fine-tuning using pre-trained models](https:\u002F\u002Fwww.learnopencv.com\u002Fkeras-tutorial-fine-tuning-using-pre-trained-models\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FKeras-Fine-Tuning)|\n|[OpenCV Transparent API](https:\u002F\u002Fwww.learnopencv.com\u002Fopencv-transparent-api\u002F)| |\n|[Face Reconstruction using EigenFaces (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Fface-reconstruction-using-eigenfaces-cpp-python\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FReconstructFaceUsingEigenFaces) |\n|[Eigenface using OpenCV (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Feigenface-using-opencv-c-python\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FEigenFace)|\n|[Principal Component Analysis](https:\u002F\u002Fwww.learnopencv.com\u002Fprincipal-component-analysis\u002F)| |\n|[Keras Tutorial : Transfer Learning using pre-trained models](https:\u002F\u002Fwww.learnopencv.com\u002Fkeras-tutorial-transfer-learning-using-pre-trained-models\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FKeras-Transfer-Learning) |\n|[Keras Tutorial : Using pre-trained Imagenet models](https:\u002F\u002Fwww.learnopencv.com\u002Fkeras-tutorial-using-pre-trained-imagenet-models\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FKeras-ImageNet-Models) |\n|[Technical Aspects of a Digital SLR](https:\u002F\u002Fwww.learnopencv.com\u002Ftechnical-aspects-of-a-digital-slr\u002F) | |\n|[Using Harry Potter interactive wand with OpenCV to create magic](https:\u002F\u002Fwww.learnopencv.com\u002Fusing-harry-potter-interactive-wand-with-opencv-to-create-magic\u002F)| |\n|[Install OpenCV 3 and Dlib on Windows ( Python only )](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-3-and-dlib-on-windows-python-only\u002F)| |\n|[Image Classification using Convolutional Neural Networks in Keras](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-classification-using-convolutional-neural-networks-in-keras)      | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FKerasCNN-CIFAR)|\n|[Understanding Autoencoders using Tensorflow (Python)](https:\u002F\u002Fwww.learnopencv.com\u002Funderstanding-autoencoders-using-tensorflow-python\u002F)      | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDenoisingAutoencoder)|\n|[Best Project Award : Computer Vision for Faces](https:\u002F\u002Fwww.learnopencv.com\u002Fbest-project-award-computer-vision-for-faces\u002F) | |\n|[Understanding Activation Functions in Deep Learning](https:\u002F\u002Fwww.learnopencv.com\u002Funderstanding-activation-functions-in-deep-learning\u002F)      | |\n|[Image Classification using Feedforward Neural Network in Keras](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-classification-using-feedforward-neural-network-in-keras\u002F)      | [Code](https:\u002F\u002Fgithub.com\u002Fkromydas\u002Flearnopencv\u002Ftree\u002Fmaster\u002FKeras-MLP-MNIST-Classification)|\n|[Exposure Fusion using OpenCV (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Fexposure-fusion-using-opencv-cpp-python\u002F)      | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FExposureFusion)|\n|[Understanding Feedforward Neural Networks](https:\u002F\u002Fwww.learnopencv.com\u002Funderstanding-feedforward-neural-networks\u002F)      | |\n|[High Dynamic Range (HDR) Imaging using OpenCV (C++\u002FPython)](http:\u002F\u002Fwww.learnopencv.com\u002Fhigh-dynamic-range-hdr-imaging-using-opencv-cpp-python)      | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fhdr)|\n|[Deep learning using Keras – The Basics](http:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-using-keras-the-basics)      | [Code](https:\u002F\u002Fgithub.com\u002Fkromydas\u002Flearnopencv\u002Ftree\u002Fmaster\u002FKeras-Linear-Regression)|\n|[Selective Search for Object Detection (C++ \u002F Python)](http:\u002F\u002Fwww.learnopencv.com\u002Fselective-search-for-object-detection-cpp-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSelectiveSearch) |\n|[Installing Deep Learning Frameworks on Ubuntu with CUDA support](http:\u002F\u002Fwww.learnopencv.com\u002Finstalling-deep-learning-frameworks-on-ubuntu-with-cuda-support\u002F) | |\n|[Parallel Pixel Access in OpenCV using forEach](http:\u002F\u002Fwww.learnopencv.com\u002Fparallel-pixel-access-in-opencv-using-foreach\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FforEach) |\n|[cvui: A GUI lib built on top of OpenCV drawing primitives](http:\u002F\u002Fwww.learnopencv.com\u002Fcvui-gui-lib-built-on-top-of-opencv-drawing-primitives\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FUI-cvui) |\n|[Install Dlib on Windows](http:\u002F\u002Fwww.learnopencv.com\u002Finstall-dlib-on-windows\u002F) | |\n|[Install Dlib on Ubuntu](http:\u002F\u002Fwww.learnopencv.com\u002Finstall-dlib-on-ubuntu\u002F) | |\n|[Install OpenCV3 on Ubuntu](http:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv3-on-ubuntu\u002F) | |\n|[Read, Write and Display a video using OpenCV ( C++\u002F Python )](http:\u002F\u002Fwww.learnopencv.com\u002Fread-write-and-display-a-video-using-opencv-cpp-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVideoReadWriteDisplay) |\n|[Install Dlib on MacOS](http:\u002F\u002Fwww.learnopencv.com\u002Finstall-dlib-on-macos\u002F) | |\n|[Install OpenCV 3 on MacOS](http:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv3-on-macos\u002F) | |\n|[Install OpenCV 3 on Windows](http:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv3-on-windows\u002F) | |\n|[Get OpenCV Build Information ( getBuildInformation )](http:\u002F\u002Fwww.learnopencv.com\u002Fget-opencv-build-information-getbuildinformation\u002F) | |\n|[Color spaces in OpenCV (C++ \u002F Python)](http:\u002F\u002Fwww.learnopencv.com\u002Fcolor-spaces-in-opencv-cpp-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FColorSpaces)|\n|[Neural Networks : A 30,000 Feet View for Beginners](http:\u002F\u002Fwww.learnopencv.com\u002Fneural-networks-a-30000-feet-view-for-beginners\u002F) | |\n|[Alpha Blending using OpenCV (C++ \u002F Python)](http:\u002F\u002Fwww.learnopencv.com\u002Falpha-blending-using-opencv-cpp-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAlphaBlending) |\n|[User stories : How readers of this blog are applying their knowledge to build applications](http:\u002F\u002Fwww.learnopencv.com\u002Fuser-stories-how-readers-of-this-blog-are-applying-their-knowledge-to-build-applications\u002F) | |\n|[How to select a bounding box ( ROI ) in OpenCV (C++\u002FPython) ?](http:\u002F\u002Fwww.learnopencv.com\u002Fhow-to-select-a-bounding-box-roi-in-opencv-cpp-python\u002F) | |\n|[Automatic Red Eye Remover using OpenCV (C++ \u002F Python)](http:\u002F\u002Fwww.learnopencv.com\u002Fautomatic-red-eye-remover-using-opencv-cpp-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FRedEyeRemover) |\n|[Bias-Variance Tradeoff in Machine Learning](http:\u002F\u002Fwww.learnopencv.com\u002Fbias-variance-tradeoff-in-machine-learning\u002F) | |\n|[Embedded Computer Vision: Which device should you choose?](http:\u002F\u002Fwww.learnopencv.com\u002Fembedded-computer-vision-which-device-should-you-choose\u002F) | |\n|[Object Tracking using OpenCV (C++\u002FPython)](http:\u002F\u002Fwww.learnopencv.com\u002Fobject-tracking-using-opencv-cpp-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Ftracking) |\n|[Handwritten Digits Classification : An OpenCV ( C++ \u002F Python ) Tutorial](http:\u002F\u002Fwww.learnopencv.com\u002Fhandwritten-digits-classification-an-opencv-c-python-tutorial\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fdigits-classification) |\n|[Training a better Haar and LBP cascade based Eye Detector using OpenCV](http:\u002F\u002Fwww.learnopencv.com\u002Ftraining-better-haar-lbp-cascade-eye-detector-opencv\u002F) | |\n|[Deep Learning Book Gift Recipients](http:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-book-gift-recipients\u002F) | |\n|[Minified OpenCV Haar and LBP Cascades](http:\u002F\u002Fwww.learnopencv.com\u002Fminified-opencv-haar-and-lbp-cascades\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FninjaEyeDetector)|\n|[Deep Learning Book Gift](http:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-book-gift\u002F) | |\n|[Histogram of Oriented Gradients](http:\u002F\u002Fwww.learnopencv.com\u002Fhistogram-of-oriented-gradients\u002F) | |\n|[Image Recognition and Object Detection : Part 1](http:\u002F\u002Fwww.learnopencv.com\u002Fimage-recognition-and-object-detection-part1\u002F) | |\n|[Head Pose Estimation using OpenCV and Dlib](http:\u002F\u002Fwww.learnopencv.com\u002Fhead-pose-estimation-using-opencv-and-dlib\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHeadPose) |\n|[Live CV : A Computer Vision Coding Application](http:\u002F\u002Fwww.learnopencv.com\u002Flive-cv\u002F) | |\n|[Approximate Focal Length for Webcams and Cell Phone Cameras](http:\u002F\u002Fwww.learnopencv.com\u002Fapproximate-focal-length-for-webcams-and-cell-phone-cameras\u002F) | |\n|[Configuring Qt for OpenCV on OSX](http:\u002F\u002Fwww.learnopencv.com\u002Fconfiguring-qt-for-opencv-on-osx\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fqt-test) |\n|[Rotation Matrix To Euler Angles](http:\u002F\u002Fwww.learnopencv.com\u002Frotation-matrix-to-euler-angles\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FRotationMatrixToEulerAngles) |\n|[Speeding up Dlib’s Facial Landmark Detector](http:\u002F\u002Fwww.learnopencv.com\u002Fspeeding-up-dlib-facial-landmark-detector\u002F) | |\n|[Warp one triangle to another using OpenCV ( C++ \u002F Python )](http:\u002F\u002Fwww.learnopencv.com\u002Fwarp-one-triangle-to-another-using-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FWarpTriangle) |\n|[Average Face : OpenCV ( C++ \u002F Python ) Tutorial](http:\u002F\u002Fwww.learnopencv.com\u002Faverage-face-opencv-c-python-tutorial\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFaceAverage) |\n|[Face Swap using OpenCV ( C++ \u002F Python )](http:\u002F\u002Fwww.learnopencv.com\u002Fface-swap-using-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFaceSwap) |\n|[Face Morph Using OpenCV — C++ \u002F Python](http:\u002F\u002Fwww.learnopencv.com\u002Fface-morph-using-opencv-cpp-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFaceMorph) |\n|[Deep Learning Example using NVIDIA DIGITS 3 on EC2](http:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-example-using-nvidia-digits-3-on-ec2\u002F) | |\n|[NVIDIA DIGITS 3 on EC2](http:\u002F\u002Fwww.learnopencv.com\u002Fnvidia-digits-3-on-ec2\u002F) | |\n|[Homography Examples using OpenCV ( Python \u002F C ++ )](http:\u002F\u002Fwww.learnopencv.com\u002Fhomography-examples-using-opencv-python-c\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHomography) |\n|[Filling holes in an image using OpenCV ( Python \u002F C++ )](http:\u002F\u002Fwww.learnopencv.com\u002Ffilling-holes-in-an-image-using-opencv-python-c\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHoles) |\n|[How to find frame rate or frames per second (fps) in OpenCV ( Python \u002F C++ ) ?](http:\u002F\u002Fwww.learnopencv.com\u002Fhow-to-find-frame-rate-or-frames-per-second-fps-in-opencv-python-cpp\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFPS) |\n|[Delaunay Triangulation and Voronoi Diagram using OpenCV ( C++ \u002F Python)](http:\u002F\u002Fwww.learnopencv.com\u002Fdelaunay-triangulation-and-voronoi-diagram-using-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDelaunay) |\n|[OpenCV (C++ vs Python) vs MATLAB for Computer Vision](http:\u002F\u002Fwww.learnopencv.com\u002Fopencv-c-vs-python-vs-matlab-for-computer-vision\u002F) | |\n|[Facial Landmark Detection](http:\u002F\u002Fwww.learnopencv.com\u002Ffacial-landmark-detection\u002F) | |\n|[Why does OpenCV use BGR color format ?](http:\u002F\u002Fwww.learnopencv.com\u002Fwhy-does-opencv-use-bgr-color-format\u002F) | |\n|[Computer Vision for Predicting Facial Attractiveness](http:\u002F\u002Fwww.learnopencv.com\u002Fcomputer-vision-for-predicting-facial-attractiveness\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFacialAttractiveness) |\n|[applyColorMap for pseudocoloring in OpenCV ( C++ \u002F Python )](http:\u002F\u002Fwww.learnopencv.com\u002Fapplycolormap-for-pseudocoloring-in-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FColormap) |\n|[Image Alignment (ECC) in OpenCV ( C++ \u002F Python )](http:\u002F\u002Fwww.learnopencv.com\u002Fimage-alignment-ecc-in-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImageAlignment) |\n|[How to find OpenCV version in Python and C++ ?](http:\u002F\u002Fwww.learnopencv.com\u002Fhow-to-find-opencv-version-python-cpp\u002F) | |\n|[Baidu banned from ILSVRC 2015](http:\u002F\u002Fwww.learnopencv.com\u002Fbaidu-banned-from-ilsvrc-2015\u002F) | |\n|[OpenCV Transparent API](http:\u002F\u002Fwww.learnopencv.com\u002Fopencv-transparent-api\u002F) | |\n|[How Computer Vision Solved the Greatest Soccer Mystery of All Time](http:\u002F\u002Fwww.learnopencv.com\u002Fhow-computer-vision-solved-the-greatest-soccer-mystery-of-all-times\u002F) | |\n|[Embedded Vision Summit 2015](http:\u002F\u002Fwww.learnopencv.com\u002Fembedded-vision-summit-2015\u002F) | |\n|[Read an Image in OpenCV ( Python, C++ )](http:\u002F\u002Fwww.learnopencv.com\u002Fread-an-image-in-opencv-python-cpp\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fimread) |\n|[Non-Photorealistic Rendering using OpenCV ( Python, C++ )](http:\u002F\u002Fwww.learnopencv.com\u002Fnon-photorealistic-rendering-using-opencv-python-c\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FNonPhotorealisticRendering) |\n|[Seamless Cloning using OpenCV ( Python , C++ )](http:\u002F\u002Fwww.learnopencv.com\u002Fseamless-cloning-using-opencv-python-cpp\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSeamlessCloning) |\n|[OpenCV Threshold ( Python , C++ )](http:\u002F\u002Fwww.learnopencv.com\u002Fopencv-threshold-python-cpp\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FThreshold) |\n|[Blob Detection Using OpenCV ( Python, C++ )](http:\u002F\u002Fwww.learnopencv.com\u002Fblob-detection-using-opencv-python-c\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FBlobDetector) |\n|[Turn your OpenCV Code into a Web API in under 10 minutes — Part 1](http:\u002F\u002Fwww.learnopencv.com\u002Fturn-your-opencv-Code-into-a-web-api-in-under-10-minutes-part-1\u002F) | |\n|[How to compile OpenCV sample Code ?](http:\u002F\u002Fwww.learnopencv.com\u002Fhow-to-compile-opencv-sample-Code\u002F) | |\n|[Install OpenCV 3 on Yosemite ( OSX 10.10.x )](http:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-3-on-yosemite-osx-10-10-x\u002F) | |\n","# LearnOpenCV\n\n此仓库包含我们在博客 [LearnOpenCV.com](https:\u002F\u002Fwww.LearnOpenCV.com) 上分享的计算机视觉、深度学习和人工智能研究文章的代码。\n\n想成为人工智能专家吗？[OpenCV 人工智能课程](https:\u002F\u002Fopencv.org\u002Fcourses\u002F) 是一个很好的起点。\n\n\u003Ca href=\"https:\u002F\u002Fopencv.org\u002Fcourses\u002F\">\n\n\u003Cp align=\"center\">\n\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fspmallick_learnopencv_readme_25bc0f803b08.png\">\n\u003C\u002Fp>\n\u003C\u002Fa>\n\n## 博客文章列表\n\n| Blog Post | Code|\n| ------------- |:-------------|\n| [RF-DETR Segmentation: Real-Time Detection & Instance Segmentation Guide](https:\u002F\u002Flearnopencv.com\u002Frf-detr-segmentation-real-time-detection-instance-segmentation-guide\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FRF_DETR_Segmentation_Demo) |\n| [YOLO26 Instance Segmentation: Pixel-Perfect AI at Real-Time Speed](https:\u002F\u002Flearnopencv.com\u002Fyolo26-instance-segmentation-pixel-perfect-ai-at-real-time-speed\u002F) | [Code](YOLO26-instance-segmentation\u002F) |\n| [Multi-Object Tracking with Roboflow Trackers and OpenCV](https:\u002F\u002Flearnopencv.com\u002Fmulti-object-tracking-with-roboflow-trackers-and-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FRoboflow_Trackers_Demo) |\n| [Real-Time Face Blur and Pixelation with OpenCV YuNet](https:\u002F\u002Flearnopencv.com\u002Fface-blur-pixelation-opencv-yunet\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFaceBlurPixelate) |\n| [Breaking the Bottleneck: Achieving Native NMS-Free Inference with YOLO26](https:\u002F\u002Flearnopencv.com\u002Fyolo26-nms-free-inference\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLO26-NMS-Free-Demo) |\n| [YOLOv26: An Object Detector Built for Real-Time Deployment](https:\u002F\u002Flearnopencv.com\u002Fyolov26-real-time-deployment\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FInference_RF-DETR_YOLO26_RT-DETR) |\n| [Beyond Transformers: A Deep Dive into HOPE](https:\u002F\u002Flearnopencv.com\u002Fhope-beyond-transformers\u002F) | |\n| [Serving SGLang: Launch a Production-Style Server](https:\u002F\u002Flearnopencv.com\u002Fsglang-a-production-server\u002F) | |\n|[Deployment on Edge: LLM Serving on Jetson using vLLM](https:\u002F\u002Flearnopencv.com\u002Fdeployment-on-edge-vllm-on-jetson\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDeployment-on-Edge-LLM-Serving-on-Jetson-using-vLLM)|\n|[Nested Learning: Is Deep Learning Architecture an Illusion?](https:\u002F\u002Flearnopencv.com\u002Fnested-learning\u002F)||\n| [How to Build a GitHub Code-Analyser Agent for Developer Productivity](https:\u002F\u002Flearnopencv.com\u002Fhow-to-build-a-github-code-analyser-agent\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHow_to_Build_a_GitHub_Code_Analyser_Agent_for_Developer_Productivity) |\n| [The Existential Problems in LLM Serving](https:\u002F\u002Flearnopencv.com\u002Fthe-existential-problems-in-llm-serving\u002F) | |\n| [SAM 3D: Foundation Model for Single-Image 3D Reconstruction](https:\u002F\u002Flearnopencv.com\u002Fsam-3d\u002F) | |\n| [SAM-3: What’s New, How It Works, and Why It Matters](https:\u002F\u002Flearnopencv.com\u002Fsam-3-whats-new\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSAM-3) |\n| [Image-GS: Adaptive Image Reconstruction using 2D Gaussians](https:\u002F\u002Flearnopencv.com\u002Fimage-gs-image-reconstruction-using-2d-gaussians\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImage_GS_Adaptive_Image_Reconstruction_using_2D_Gaussians) |\n| [Ultimate Guide to Vector Databases and RAG Pipeline](https:\u002F\u002Flearnopencv.com\u002Fvector-db-and-rag-pipeline-for-document-rag\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FUltimate_Guide_to_Vector_Databases_and_RAG_pipeline) |\n|[What Makes DeepSeek OCR So Powerful](https:\u002F\u002Flearnopencv.com\u002Fwhat-makes-deepseek-ocr-so-powerful\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FWhat-Makes-DeepSeek-OCR-So-Powerful)|\n| [2D Gaussian Splatting: Geometrically Accurate Radiance Field Reconstruction](https:\u002F\u002Flearnopencv.com\u002F2d-gaussian-splatting-2dgs\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002F2D_Gaussian_Splatting_Geometrically_Accurate_Radiance_Field_Reconstruction) |\n| [TRM: Tiny Recursive Models](https:\u002F\u002Flearnopencv.com\u002Ftrm-tiny-ai-models-outsmarting-giants-on-complex-puzzles\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTRM) |\n|[Deploying ML Models on Arduino: From Blink to Think](https:\u002F\u002Flearnopencv.com\u002Fdeploying-ml-on-arduino\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDeploying-ML-Models-on-Arduino-From-Blink-to-Think)|\n| [VideoRAG: Redefining Long-Context Video Comprehension](https:\u002F\u002Flearnopencv.com\u002Fvideorag-long-context-video-comprehension\u002F) | |\n| [AI Agent in Action: Automating Desktop Tasks with VLMs](https:\u002F\u002Flearnopencv.com\u002Fbuild-ai-agents-using-vlm\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FLocal-VLM-Agents-in-Action-GUI-Automation-with-Moondream3-and-Gemini) |\n| [Top VLM Evaluation Metrics for Optimal Performance Analysis](https:\u002F\u002Flearnopencv.com\u002Fvlm-evaluation-metrics\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVLM_Evaluation_Metrics) |\n|[Getting Started with VLM on Jetson Nano](https:\u002F\u002Flearnopencv.com\u002Fvlm-on-jetson-nano\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FGetting-Started-with-VLM-on-Jetson-Nano)|\n| [VLM on Edge: Worth the Hype or Just a Novelty?](https:\u002F\u002Flearnopencv.com\u002Fvlm-on-edge-devices\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVLM-on-Edge-Worth-the-Hype-or-Just-a-Novelty) |\n| [AnomalyCLIP : Harnessing CLIP for Weakly-Supervised Video Anomaly Recognition](https:\u002F\u002Flearnopencv.com\u002Fanomalyclip-video-anomaly-recognition\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAnomalyCLIP_Harnessing_CLIP_for_Weakly_Supervised_Video_Anomaly_Recognition) |\n| [AI_for_Video_Understanding_From_Content_Moderation_to_Summarization](https:\u002F\u002Flearnopencv.com\u002Fai-for-video-understanding\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAI_for_Video_Understanding_From_Content_Moderation_to_Summarization) |\n| [Video-RAG: Training-Free Retrieval for Long-Video LVLMs](https:\u002F\u002Flearnopencv.com\u002Fvideo-rag-for-long-videos\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVideo-RAG_Training_Free_Retrieval_for_Long_Video_LVLMs) |\n| [Object Detection and Spatial Understanding with VLMs ft. Qwen2.5-VL](https:\u002F\u002Flearnopencv.com\u002Fobject-detection-with-vlms-ft-qwen2-5-vl\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fobject-detection-with-vlms) |\n| [LangGraph: Building Self-Correcting RAG Agent for Code Generation](https:\u002F\u002Flearnopencv.com\u002Flanggraph-self-correcting-agent-code-generation\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FLangGraph_Building_Self_Correcting_RAG_Agent_for_Code_Generation) |\n| [Inside Sinusoidal Position Embeddings: A Sense of Order](https:\u002F\u002Flearnopencv.com\u002Fsinusoidal-position-embeddings\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSinusoidal_Position_Embeddings) |\n| [Inside RoPE: Rotary Magic into Position Embeddings](https:\u002F\u002Flearnopencv.com\u002Frope-position-embeddings\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FInside_RoPE_Position_Embeddings) |\n| [SimLingo-Vision-Language-Action-Model-for-Autonomous-Driving](https:\u002F\u002Flearnopencv.com\u002Fsimlingo-vision-language-action-model-for-autonomous-driving\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSimLingo-Vision-Language-Action-Model-for-Autonomous-Driving) |\n| [FineTuning Gemma 3n for Medical VQA on ROCOv2](https:\u002F\u002Flearnopencv.com\u002Ffinetuning-gemma-3n-medical-vqa\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Ffinetuning-gemma3n) |\n| [SmolLM3 Blueprint: SOTA 3B-Parameter LLM](https:\u002F\u002Flearnopencv.com\u002Fsmollm3-explained\u002F) | |\n| [LangGraph-A-Visual-Automation-and-Summarization-Pipeline](https:\u002F\u002Flearnopencv.com\u002Flanggraph-building-a-visual-web-browser-agent\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FLangGraph-A-Visual-Automation-and-Summarization-Pipeline) |\n| [Fine-Tuning AnomalyCLIP: Class-Agnostic Zero-Shot Anomaly Detection](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-anomalyclip-medical-anomaly-clip\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-AnomalyCLIP) |\n| [SigLIP 2: DeepMind’s Multilingual Vision-Language Model](https:\u002F\u002Flearnopencv.com\u002Fsiglip-2-deepminds-multilingual-vision-language-model\u002F) | |\n| [MedGemma: Google’s Medico VLM for Clinical QA, Imaging, and More](https:\u002F\u002Flearnopencv.com\u002Fmedgemma-explained\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fmedgemma) |\n| [Nanonets-OCR-s: Enabling Rich, Structured Markdown for Document Understanding](https:\u002F\u002Flearnopencv.com\u002Fnanonets-ocr-s\u002F) | |\n| [Optimizing VJEPA-2: Tackling Latency & Context in Real-Time Video Classification Scripts](https:\u002F\u002Flearnopencv.com\u002Foptimizing-vjepa-2-in-real-time-video-classification\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVJEPA-2-Video-Classification) |\n| [V-JEPA 2: Meta’s Breakthrough in AI for the Physical World](https:\u002F\u002Flearnopencv.com\u002F?p=73731&preview_id=73731&preview_nonce=beb70ccf8e&preview=true#heading-7) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FV-JEPA-2) |\n| [NVIDIA Cosmos Reason1: Video Understanding](https:\u002F\u002Flearnopencv.com\u002Fcosmos-reason-vlm-video-vqa\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FCosmos-Reason1-Video-Understanding) |\n| [GR00T N1.5 Explained](https:\u002F\u002Flearnopencv.com\u002Fgr00t-n1_5-explained\u002F) |  |\n| [LLaVA](https:\u002F\u002Flearnopencv.com\u002Fllava-training-a-visual-assistant\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FLLaVA) |\n| [SmolVLA: Affordable & Efficient VLA Robotics on Consumer GPUs](https:\u002F\u002Flearnopencv.com\u002Fsmolvla-lerobot-vision-language-action-model\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fsmolvla) |\n| [Fine-Tuning Grounding DINO: Open-Vocabulary Object Detection](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-grounding-dino\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-Grounding-DINO-Open-Vocabulary-Object-Detection) |\n| [Getting Started with Qwen3 – The Thinking Expert](https:\u002F\u002Flearnopencv.com\u002Fqwen3\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fqwen3) |\n| [Inside the GPU: A Comprehensive Guide to Modern Graphics Architecture](https:\u002F\u002Flearnopencv.com\u002Fmodern-gpu-architecture-explained\u002F) | |\n| [Distributed Parallel Training: PyTorch](https:\u002F\u002Flearnopencv.com\u002Fdistributed-parallel-training-pytorch-multi-gpu-setup\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDistributed-Training-PyTorch) |\n| [MONAI: The Definitive Framework for Medical Imaging Powered by PyTorch](https:\u002F\u002Flearnopencv.com\u002Fmonai-medical-imaging-pytorch\u002F) | |\n| [SANA-Sprint: The One-Step Revolution in High-Quality AI Image Synthesis](https:\u002F\u002Flearnopencv.com\u002Fsana-sprint-the-one-step-revolution-in-high-quality-ai-image-synthesis\u002F) | |\n| [FramePack-Video-Diffusion-but-feels-like-Image-Diffusion](https:\u002F\u002Flearnopencv.com\u002Fframepack-video-diffusion\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFramePack-Video-Diffusion-but-feels-like-Image-Diffusion) |\n| [Model Weights File Formats in Machine Learning](https:\u002F\u002Flearnopencv.com\u002Fmodel-weights-file-formats-in-machine-learning\u002F) | |\n| [Unsloth: A Guide from Basics to Fine-Tuning Vision Models](https:\u002F\u002Flearnopencv.com\u002Funsloth-guide-efficient-llm-fine-tuning\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FUnsloth_A_Guide_From_Basics_to_Fine_Tuning_Vision_Models) |\n| [Iterative Closest Point (ICP) Algorithm Explained](https:\u002F\u002Flearnopencv.com\u002Fiterative-closest-point-icp-explained\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FIterative-Closest-Point-ICP) |\n| [MedSAM2 Explained: One Prompt to Segment Anything in Medical Imaging](https:\u002F\u002Flearnopencv.com\u002Fmedsam2-explained\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002Fmedsam2-explained) |\n| [Batch Normalization and Dropout as Regularizers](https:\u002F\u002Flearnopencv.com\u002Fbatch-normalization-and-dropout-as-regularizers\u002F) | |\n| [DINOv2_by_Meta_A_Self-Supervised_foundational_vision_model](https:\u002F\u002Flearnopencv.com\u002Fdinov2-self-supervised-vision-transformer\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FDINOv2_by_Meta_A_Self-Supervised_foundational_vision_model) |\n| [Beginner's Guide to Embedding Models](https:\u002F\u002Flearnopencv.com\u002Fembedding-models-explained\u002F) | |\n| [MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors](https:\u002F\u002Flearnopencv.com\u002Fmast3r-slam-realtime-dense-slam-explained\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FMASt3R-SLAM) |\n| [Google's A2A Protocol](https:\u002F\u002Flearnopencv.com\u002Fgoogles-a2a-protocol-heres-what-you-need-to-know\u002F) | |\n| [Nvidia SANA : Faster Image Generation](https:\u002F\u002Flearnopencv.com\u002Fnvidia-sana-image-generation-model\u002F) | |\n| [Fine-tuning RF-DETR](https:\u002F\u002Flearnopencv.com\u002Frf-detr-object-detection\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FFine-tuning-RF-DETR) |\n| [Qwen2.5-Omni: A Real-Time Multimodal AI](https:\u002F\u002Flearnopencv.com\u002Fqwen2.5-omni\u002F) | |\n| [Vision Language Action Models: Robotic Control](https:\u002F\u002Flearnopencv.com\u002Fvision-language-action-models-lerobot-policy\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVision-Language-Action-Models) |\n| [Fine-Tuning Gemma 3 VLM using QLoRA for LaTeX-OCR Dataset](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-gemma-3\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-Gemma-3-VLM-using-QLoRA-for-LaTeX-OCR-Dataset) |\n| [ComfyUI](https:\u002F\u002Flearnopencv.com\u002Fintroduction-to-comfyui-for-stable-diffusion\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FComfyUI) |\n| [Gemma-3: A Comprehensive Introduction](https:\u002F\u002Flearnopencv.com\u002Fgemma-3\u002F) | |\n| [YOLO11 on Raspberry Pi: Optimizing Object Detection for Edge Devices](https:\u002F\u002Flearnopencv.com\u002Fyolo11-on-raspberry-pi\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fyolo11-on-raspberry-pi) |\n| [VGGT: Visual Geometry Grounded Transformer – For Dense 3D Reconstruction](https:\u002F\u002Flearnopencv.com\u002Fvggt-visual-geometry-grounded-transformer-3d-reconstruction\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVGGT-3D-Reconstruction) |\n| [DDIM: The Faster, Improved Version of DDPM for Efficient AI Image Generation](https:\u002F\u002Flearnopencv.com\u002Funderstanding-ddim\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDDIM-The-Faster-Improved-Version-of-DDPM-for-Efficient-AI-Image-Generation) |\n| [Introduction to Model Context Protocol (MCP)](https:\u002F\u002Flearnopencv.com\u002Fintroduction-to-model-context-protocol\u002F) | |\n| [MASt3R and MASt3R-SfM Explanation: Image Matching and 3D Reconstruction](https:\u002F\u002Flearnopencv.com\u002Fmast3r-sfm-grounding-image-matching-3d\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMASt3R-SfM-3D-Reconstruction-Image-Matching) |\n| [MatAnyone Explained: Consistent Memory for Better Video Matting](https:\u002F\u002Flearnopencv.com\u002Fmatanyone-for-better-video-matting\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMatAnyone-Explained-Consistent-Memory-for-Better-Video-Matting) |\n| [GraphRAG: For Medical Document Analysis](https:\u002F\u002Flearnopencv.com\u002Fgraphrag-explained-knowledge-graphs-medical\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FGraphrag-Medical-Document-Analysis) |\n| [OmniParser: Vision Based GUI Agent](https:\u002F\u002Flearnopencv.com\u002Fomniparser-vision-based-gui-agent\u002F) | |\n| [Fine-Tuning-YOLOv12-Comparison-With-YOLOv11-And-YOLOv7-Based-Darknet](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-yolov12\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-YOLOv12-Comparison-With-YOLOv11-And-YOLOv7-Based-Darknet) |\n| [FineTuning RetinaNet for Wildlife Detection with PyTorch: A Step-by-Step Tutorial](https:\u002F\u002Flearnopencv.com\u002Ffinetuning-retinanet) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Ffinetuning-retinanet) |\n| [DUSt3R: Geometric 3D Vision Made Easy :  Explanation and Results](https:\u002F\u002Flearnopencv.com\u002Fdust3r-geometric-3d-vision\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDUSt3R-Dense-3D-Reconstruction) |\n| [YOLOv12: Attention Meets Speed](https:\u002F\u002Flearnopencv.com\u002Fyolov12) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv12) |\n| [Video Generation: A Diffusion based approach](https:\u002F\u002Flearnopencv.com\u002Fvideo-generation-models\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVideo-Generation-A-Diffusion-based-approach) |\n| [Agentic AI: A Comprehensive Introduction](https:\u002F\u002Flearnopencv.com\u002Fagentic-ai\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAgentic-AI-A-Comprehensive-Introduction) |\n| [Finetuning SAM2 for Leaf Disease Segmentation](https:\u002F\u002Flearnopencv.com\u002Ffinetuning-sam2\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Ffinetuning-sam2) |\n| [Object Insertion in Gaussian Splatting: Paper Explained and Training Code for MCMC and Bilateral Grid](https:\u002F\u002Flearnopencv.com\u002Fobject-insertion-in-gaussian-splatting\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FObject-Insertion-in-Gaussian-Splatting) |\n| [Depth Pro: Sharp Monocular Metric Depth](https:\u002F\u002Flearnopencv.com\u002Fdepth-pro-monocular-metric-depth) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDepthPro-Monocular-Metric-Depth) |\n| [Fine-tuning-Stable-Diffusion-3_5-UI-images](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-stable-diffusion-3-5m\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-tuning-Stable-Diffusion-3_5-UI-images) |\n| [SimSiam: Streamlining SSL with Stop-Gradient Mechanism](https:\u002F\u002Flearnopencv.com\u002Fsimsiam\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSimSiam-Streamlining-SSL-with-Stop-Gradient-Mechanism) |\n| [Image Captioning using ResNet and LSTM](https:\u002F\u002Flearnopencv.com\u002Fimage-captioning\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImage-Captioning-using-ResNet-and-LSTM) |\n| [Molmo VLM: Paper Explanation and Demo](https:\u002F\u002Flearnopencv.com\u002Fmolmo-vlm) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMolmo-VLM-SAM2) |\n| [3D Gaussian Splatting Paper Explanation: Training Custom Datasets with NeRF-Studio Gsplats](https:\u002F\u002Flearnopencv.com\u002F3d-gaussian-splatting\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002F3D-Gaussian-Splatting-Code) |\n| [FLUX Image Generation: Experimenting with the Parameters](https:\u002F\u002Flearnopencv.com\u002Fflux-ai-image-generator\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFlux-Image-Generation) |\n| [Contrastive-Learning-SimCLR-and-BYOL(With Code Example)](https:\u002F\u002Flearnopencv.com\u002Fcontrastive-learning-simclr-and-byol-with-code-example\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FContrastive-Learning-SimCLR-and-BYOL) |\n| [The Annotated NeRF : Training on Custom Dataset from Scratch in Pytorch](https:\u002F\u002Flearnopencv.com\u002Fannotated-nerf-pytorch\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAnnotated-NeRF) |\n| [Stable Diffusion 3 and 3.5: Paper Explanation and Inference](https:\u002F\u002Flearnopencv.com\u002Fstable-diffusion-3\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FStable-Diffusion-3) |\n| [LightRAG - Legal Document Analysis](https:\u002F\u002Flearnopencv.com\u002Flightrag\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FLightRAG-Legal) |\n| [NVIDIA AI Summit 2024 – India Overview](https:\u002F\u002Flearnopencv.com\u002Fnvidia-ai-summit-2024-india-overview\u002F) | |\n| [Introduction to Speech to Speech: Most Efficient Form of NLP](https:\u002F\u002Flearnopencv.com\u002Fspeech-to-speech\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fspeech-to-speech) |\n| [Training 3D U-Net for Brain Tumor Segmentation (BraTS-GLI)](https:\u002F\u002Flearnopencv.com\u002F3d-u-net-brats\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTraining_3D_U-Net_Brain_Tumor_Seg) |\n| [DETR: Overview and Inference](https:\u002F\u002Flearnopencv.com\u002Fdetr-overview-and-inference\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDETR-Overview_and_Inference) |\n| [YOLO11: Faster Than You Can Imagine!](https:\u002F\u002Flearnopencv.com\u002Fyolo11\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLO11) |\n| [Exploring DINO: Self-Supervised Transformers for Road Segmentation with ResNet50 and U-Net](https:\u002F\u002Flearnopencv.com\u002Ffine-tune-dino-self-supervised-learning-segmentation\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FExploring-DINO-dino-road-segmentation) |\n| [Sapiens: Foundation for Human Vision Models by Meta](https:\u002F\u002Flearnopencv.com\u002Fsapiens-human-vision-models) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSapiens-Human-Vision-Model-Meta) |\n| [Multimodal RAG with ColPali and Gemini](https:\u002F\u002Flearnopencv.com\u002Fmultimodal-rag-with-colpali) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMultimodal-RAG-with-ColPali-Gemini) |\n| [Building Autonomous Vehicle in Carla: Path Following with PID Control & ROS 2](https:\u002F\u002Flearnopencv.com\u002Fpid-controller-ros-2-carla\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FBuilding_Autonomous_Vehicle_in_Carla_Path_Following_with_PID_Control_ROS2) |\n| [Handwritten Text Recognition using OCR](https:\u002F\u002Flearnopencv.com\u002Fhandwritten-text-recognition-using-ocr\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHandwritten_Text_Recognition_using_OCR) |\n| [Training CLIP from Sratch for Image Retrieval](https:\u002F\u002Flearnopencv.com\u002Fclip-model) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTraining-CLIP-from-Scratch-for-Image-Retrieval) |\n| [Introduction to LiDAR SLAM: LOAM and LeGO-LOAM Paper and Code Explanation with ROS 2 Implementation](https:\u002F\u002Flearnopencv.com\u002Flidar-slam-with-ros2) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FLeGO-LOAM-ROS2) |\n| [Recommendation System using Vector Search](https:\u002F\u002Flearnopencv.com\u002Frecommendation-system-using-vector-search) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FRecommendation-System-using-Vector-Search) |\n| [Fine Tuning Whisper on Custom Dataset](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-whisper-on-custom-dataset\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-Whisper-on-Custom-Dataset) |\n| [SAM 2 – Promptable Segmentation for Images and Videos](https:\u002F\u002Flearnopencv.com\u002Fsam-2\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSAM_2_Segment_Anything_Model_2) |\n| [Introduction to Feature Matching Using Neural Networks](https:\u002F\u002Flearnopencv.com\u002Ffeature-matching\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFeature-Matching-Using-Neural-Networks) |\n| [Introduction to ROS2 (Robot Operating System 2): Tutorial on ROS2 Working, DDS, ROS1 RMW, Topics, Nodes, Publisher, Subscriber in Python](https:\u002F\u002Flearnopencv.com\u002Frobot-operating-system-introduction) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntroduction-to-ROS2-in-python) |\n| [CVPR 2024 Research Papers - Part- 2](https:\u002F\u002Flearnopencv.com\u002Fcvpr-2024-research-papers) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fcvpr-2024-research-papers-part2) |\n| [CVPR 2024: An Overview and Key Papers](https:\u002F\u002Flearnopencv.com\u002Fcvpr2024\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FCVPR-2024) |\n| [Object Detection on Edge Device - OAK-D-Lite](https:\u002F\u002Flearnopencv.com\u002Fobject-detection-on-edge-device) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FObject-Detection-on-Edge-Devices) |\n| [Fine-Tuning YOLOv10 Models on Custom Dataset](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-yolov10\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-YOLOv10-Models-Custom-Dataset) |\n| [ROS2 and Carla Setup Guide for Ubuntu 22.04](https:\u002F\u002Flearnopencv.com\u002Fros2-and-carla-setup-guide\u002F) |  |\n| [Understanding Visual SLAM for Robotics Perception: Building Monocular SLAM from Scratch in Python](https:\u002F\u002Flearnopencv.com\u002Fmonocular-slam-in-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMonocular%20SLAM%20for%20Robotics%20implementation%20in%20python) |\n| [Enhancing Image Segmentation using U2-Net: An Approach to Efficient Background Removal](https:\u002F\u002Flearnopencv.com\u002Fu2-net-image-segmentation\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FEfficient-Background-Removal-using-U2-Net) |\n| [YOLOv10: The Dual-Head OG of YOLO Series](https:\u002F\u002Flearnopencv.com\u002Fyolov10\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv10) |\n| [Fine-tuning Faster R-CNN on Sea Rescue Dataset](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-faster-r-cnn\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-tuning-Faster-R-CNN-on-SeaRescue-Dataset) |\n| [Mastering Recommendation System: A Complete Guide](https:\u002F\u002Flearnopencv.com\u002Frecommendation-system\u002F) | |\n| [Automatic Speech Recognition with Diarization : Speech-to-Text](https:\u002F\u002Flearnopencv.com\u002Fautomatic-speech-recognition\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAutomatic-Speech-Recognition-with-Diarization-Speech-to-Text) |\n| [Building MobileViT Image Classification Model from Scratch In Keras 3](https:\u002F\u002Flearnopencv.com\u002Fmobilevit-keras-3\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FBuilding%20MobileViT%20from%20Scratch%20in%20Keras%203) |\n| [SDXL Inpainting: Fusing Image Inpainting with Stable Diffusion](https:\u002F\u002Flearnopencv.com\u002Fsdxl-inpainting\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSDXL-inpainting) |\n| [YOLOv9 Instance Segmentation on Medical Dataset](https:\u002F\u002Flearnopencv.com\u002Fyolov9-instance-segmentation-on-medical-dataset\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv9-Instance-Segmentation-on-Medical-Dataset) |\n| [A Comprehensive Guide to Robotics](https:\u002F\u002Flearnopencv.com\u002Fa-comprehensive-guide-to-robotics\u002F) | |\n| [Integrating Gradio with OpenCV DNN](https:\u002F\u002Flearnopencv.com\u002Fintegrating-gradio-with-opencv-dnn\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntegrating-Gradio-with-OpenCV-DNN) |\n| [Fine-Tuning YOLOv9 on Custom Dataset](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-yolov9\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-YOLOv9-Models-Custom-Dataset) |\n| [Dreambooth using Diffusers](https:\u002F\u002Flearnopencv.com\u002Fdreambooth-using-diffusers\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDreambooth_using_Diffusers) |\n| [Introduction to Hugging Face Diffusers](https:\u002F\u002Flearnopencv.com\u002Fhugging-face-diffusers\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntroduction_to_Diffusers) |\n| [Introduction to Ultralytics Explorer API](https:\u002F\u002Flearnopencv.com\u002Fultralytics-explorer-api\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntroduction-to-Ultralytics-Explorer-API) |\n| [YOLOv9: Advancing the YOLO Legacy](https:\u002F\u002Flearnopencv.com\u002Fyolov9-advancing-the-yolo-legacy\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv9-Advancing-the-YOLO-Legacy) |\n| [Fine-Tuning LLMs using PEFT](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-llms-using-peft\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-LLMs-using-PEFT) |\n| [Depth Anything: Accelerating Monocular Depth Perception](https:\u002F\u002Flearnopencv.com\u002Fdeciphering-llms\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDepth-Anything) |\n| [Deciphering LLMs: From Transformers to Quantization](https:\u002F\u002Flearnopencv.com\u002Fdeciphering-llms\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDeciphering-LLMs) |\n| [YOLO Loss Function Part 2: GFL and VFL Loss](https:\u002F\u002Flearnopencv.com\u002Fyolo-loss-function-gfl-vfl-loss\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLO-Loss-Functions-Part2) |\n| [YOLOv8-Object-Tracking-and-Counting-with-OpenCV](https:\u002F\u002Flearnopencv.com\u002Fyolov8-object-tracking-and-counting-with-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv8-Object-Tracking-and-Counting-with-OpenCV) |\n| [Stereo Vision in ADAS: Pioneering Depth Perception Beyond LiDAR](https:\u002F\u002Flearnopencv.com\u002Fadas-stereo-vision\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FADAS-Stereo-Vision) |\n| [YOLO Loss Function Part 1: SIoU and Focal Loss](https:\u002F\u002Flearnopencv.com\u002Fyolo-loss-function-siou-focal-loss\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLO-Loss-Functions-Part1) |\n| [Moving Object Detection with OpenCV](https:\u002F\u002Flearnopencv.com\u002Fmoving-object-detection-with-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMoving-Object-Detection-with-OpenCV) |\n| [Integrating ADAS with Keypoint Feature Pyramid Network for 3D LiDAR Object Detection](https:\u002F\u002Flearnopencv.com\u002F3d-lidar-object-detection\u002F) | [Code](https:\u002F\u002Fwww.dropbox.com\u002Fscl\u002Ffi\u002F3n1s68jtfkjmw2f5e5ctv\u002F3D-LiDAR-Object-Detection.zip?rlkey=d8q6xvlxis4oxso4qki87omvc&dl=1) |\n| [Mastering All YOLO Models from YOLOv1 to YOLO-NAS: Papers Explained (2024)](https:\u002F\u002Flearnopencv.com\u002Fmastering-all-yolo-models) | |\n| [GradCAM: Enhancing Neural Network Interpretability in the Realm of Explainable AI](https:\u002F\u002Flearnopencv.com\u002Fintro-to-gradcam\u002F) | [Code](https:\u002F\u002Fwww.dropbox.com\u002Fscl\u002Ffo\u002F3p3sg5fnvhrvi9vp00i0w\u002Fh?rlkey=1x01uz5o7esex7p6c8r534iyn&dl=1) |\n| [Text Summarization using T5: Fine-Tuning and Building Gradio App](https:\u002F\u002Flearnopencv.com\u002Ftext-summarization-using-t5\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FText-Summarization-using-T5-Fine-Tuning-and-Building-Gradio-App) |\n| [3D LiDAR Visualization using Open3D: A Case Study on 2D KITTI Depth Frames for Autonomous Driving](https:\u002F\u002Flearnopencv.com\u002F3d-lidar-visualization\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002F3D-LiDAR-Perception) |\n| [Fine Tuning T5: Text2Text Transfer Transformer for Building a Stack Overflow Tag Generator](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-t5\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-T5-Text2Text-Transformer-for-Strack-Overflow-Tag-Generation) |\n| [SegFormer 🤗 : Fine-Tuning for Improved Lane Detection in Autonomous Vehicles](https:\u002F\u002Flearnopencv.com\u002Fsegformer-fine-tuning-for-lane-detection) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-SegFormer-For-Lane-Detection) |\n| [Fine-Tuning BERT using Hugging Face Transformers](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-bert) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-BERT-using-Hugging-Face-Transformers) |\n| [YOLO-NAS Pose](https:\u002F\u002Flearnopencv.com\u002Fyolo-nas-pose) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLO-NAS-Pose) |\n| [BERT: Bidirectional Encoder Representations from Transformers](https:\u002F\u002Flearnopencv.com\u002Fbert-bidirectional-encoder-representations-from-transformers\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FBERT-Bidirectional-Encoder-Representations-from-Transformers) |\n| [Comparing KerasCV YOLOv8 Models on the Global Wheat Data 2020](https:\u002F\u002Flearnopencv.com\u002Fcomparing-kerascv-yolov8-models\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FComparing-KerasCV-YOLOv8-Models-on-the-Global-Wheat-Data-2020) |\n| [Top 5 AI papers of September 2023](https:\u002F\u002Flearnopencv.com\u002Ftop-5-ai-papers-of-september-2023\u002F) | |\n| [Empowering Drivers: The Rise and Role of Advanced Driver Assistance Systems](https:\u002F\u002Flearnopencv.com\u002Fadvanced-driver-assistance-systems\u002F) | |\n| [Semantic Segmentation using KerasCV DeepLabv3+](https:\u002F\u002Flearnopencv.com\u002Fkerascv-deeplabv3-plus-semantic-segmentation\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSemantic-Segmentation-using-KerasCV-with-DeepLabv3-Plus) |\n| [Object Detection using KerasCV YOLOv8](https:\u002F\u002Flearnopencv.com\u002Fobject-detection-using-kerascv-yolov8\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FObject-Detection-using-KerasCV-YOLOv8) |\n| [Fine-tuning YOLOv8 Pose Models for Animal Pose Estimation](https:\u002F\u002Flearnopencv.com\u002Fanimal-pose-estimation\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-tuning-YOLOv8-Pose-Models-for-Animal-Pose-Estimation) |\n| [Top 5 AI papers of August 2023](https:\u002F\u002Flearnopencv.com\u002Ftop-5-ai-papers-of-august-2023\u002F) | |\n| [Fine Tuning TrOCR - Training TrOCR to Recognize Curved Text](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-trocr-training-trocr-to-recognize-curved-text\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-TrOCR) |\n| [TrOCR - Getting Started with Transformer Based OCR](https:\u002F\u002Flearnopencv.com\u002Ftrocr-getting-started-with-transformer-based-ocr\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTrOCR-Getting-Started-with-Transformer-Based-OCR) |\n| [Facial Emotion Recognition](https:\u002F\u002Flearnopencv.com\u002Ffacial-emotion-recognition\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFacial-Emotion-Recognition) |\n| [Object Keypoint Similarity in Keypoint Detection](https:\u002F\u002Flearnopencv.com\u002Fobject-keypoint-similarity\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FObject-Keypoint-Similarity-in-Keypoint-Detection) |\n| [Real Time Deep SORT with Torchvision Detectors](https:\u002F\u002Flearnopencv.com\u002Freal-time-deep-sort-with-torchvision-detectors\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FReal_Time_Deep_SORT_using_Torchvision_Detectors) |\n| [Top 5 AI papers of July 2023](https:\u002F\u002Flearnopencv.com\u002Ftop-5-ai-papers-of-july-2023\u002F) | |\n| [Medical Image Segmentation](https:\u002F\u002Flearnopencv.com\u002Fmedical-image-segmentation\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMedical-Image-Segmentation-Using-HuggingFace-&-PyTorch) |\n| [Weighted Boxes Fusion in Object Detection: A Comparison with Non-Maximum Suppression](https:\u002F\u002Flearnopencv.com\u002Fweighted-boxes-fusion\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FWeighted-Boxes-Fusion-in-Object-Detection) |\n| [Medical Multi-label Classification with PyTorch & Lightning](https:\u002F\u002Flearnopencv.com\u002Fmedical-multi-label\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMedical_Multi-label_Classification_with_PyTorch_&_Lightning) |\n| [Getting Started with PaddlePaddle: Exploring Object Detection, Segmentation, and Keypoints](https:\u002F\u002Flearnopencv.com\u002Fpaddlepaddle\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntroduction-to-PaddlePaddle) |\n| [Drone Programming With Computer Vision A Beginners Guide](https:\u002F\u002Flearnopencv.com\u002Fdrone-programming-with-computer-vision\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDrone-Programming-With-Computer-Vision-A-Beginners-Guide) |\n| [How to Build a Pip Installable Package & Upload to PyPi](https:\u002F\u002Flearnopencv.com\u002Fbuilding-pip-installable-package-pypi\u002F) | |\n| [IoU Loss Functions for Faster & More Accurate Object Detection](https:\u002F\u002Flearnopencv.com\u002Fiou-loss-functions-object-detection\u002F) | |\n| [Exploring Slicing Aided Hyper Inference for Small Object Detection](https:\u002F\u002Flearnopencv.com\u002Fslicing-aided-hyper-inference\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FExploring-Slicing-Aided-Hyper-Inference) |\n| [Advancements in Face Recognition Models, Toolkit and Datasets](https:\u002F\u002Flearnopencv.com\u002Fface-recognition-models\u002F) | |\n| [Train YOLO NAS on Custom Dataset](https:\u002F\u002Flearnopencv.com\u002Ftrain-yolo-nas-on-custom-dataset\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTrain-YOLO-NAS-on-Custom-Dataset) |\n| [Train YOLOv8 Instance Segmentation on Custom Data](https:\u002F\u002Flearnopencv.com\u002Ftrain-yolov8-instance-segmentation\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTrain-YOLOv8-Instance-Segmentation-on-Custom-Data) |\n| [YOLO-NAS: New Object Detection Model Beats YOLOv6 & YOLOv8](https:\u002F\u002Flearnopencv.com\u002Fyolo-nas\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLO-NAS_Introduction) |\n| [Segment Anything – A Foundation Model for Image Segmentation](https:\u002F\u002Flearnopencv.com\u002Fsegment-anything\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSegment-Anything-A-Foundation-Model-for-Image-Segmentation) |\n|[Build a Video to Slides Converter Application using the Power of Background Estimation and Frame Differencing in OpenCV](https:\u002F\u002Flearnopencv.com\u002Fvideo-to-slides-converter-using-background-subtraction\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FBuild-a-Video-to-Slides-Converter-Application-using-the-Power-of-Background-Estimation-and-Frame-Differencing-in-OpenCV)|\n|[A Closer Look at CVAT: Perfecting Your Annotations](https:\u002F\u002Flearnopencv.com\u002Fa-closer-look-at-cvat-perfecting-your-annotations\u002F)|[YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=yxX_0-zr-2U&list=PLfYPZalDvZDLvFhjuflhrxk_lLplXUqqB)|\n| [ControlNet - Achieving Superior Image Generation Results](https:\u002F\u002Flearnopencv.com\u002Fcontrolnet\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FControlNet-Achieving-Superior-Image-Generation-Results) |\n| [InstructPix2Pix - Edit Images With Prompts](https:\u002F\u002Flearnopencv.com\u002Finstructpix2pix\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FInstructPix2Pix-Edit-Images-With-Prompts) |\n| [NVIDIA Spring GTC 2023 Day 4: Ending on a High Note with Top Moments from the Finale!](https:\u002F\u002Flearnopencv.com\u002Fnvidia-spring-gtc-2023-day-4\u002F) | |\n| [NVIDIA Spring GTC 2023 Day 3: Digging deeper into Deep Learning, Semiconductors & more!](https:\u002F\u002Flearnopencv.com\u002Fnvidia-spring-gtc-2023-day-3-digging-deeper-into-deep-learning-semiconductors-more\u002F) | |\n| [NVIDIA Spring GTC 2023 Day 2: Jensen’s keynote & the iPhone moment of AI is here!](https:\u002F\u002Flearnopencv.com\u002Fnvidia-spring-gtc-2023-day-2-jensens-keynote-the-iphone-moment-of-ai-is-here\u002F) | |\n| [NVIDIA Spring GTC 2023 Day 1: Welcome to the future!](https:\u002F\u002Flearnopencv.com\u002Fnvidia-spring-gtc-2023-day-1-highlights-welcome-to-the-future\u002F) | |\n| [NVIDIA GTC Spring 2023 Curtain Raiser](https:\u002F\u002Flearnopencv.com\u002Fnvidia-gtc-spring-2023-curtain-raiser\u002F) | |\n| [Stable Diffusion - A New Paradigm in Generative AI](https:\u002F\u002Flearnopencv.com\u002Fstable-diffusion-generative-ai\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FStable-Diffusion-A-New-Paradigm-in-Generative-AI) |\n| [OpenCV Face Recognition – Does Face Recognition Work on AI-Generated Images?](https:\u002F\u002Flearnopencv.com\u002Fopencv-face-recognition-api\u002F) | |\n|[An In-Depth Guide to Denoising Diffusion Probabilistic Models – From Theory to Implementation](https:\u002F\u002Flearnopencv.com\u002Fdenoising-diffusion-probabilistic-models\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FGuide-to-training-DDPMs-from-Scratch)|\n|[From Pixels to Paintings: The Rise of Midjourney AI Art](https:\u002F\u002Flearnopencv.com\u002Frise-of-midjourney-ai-art\u002F)| |\n|[Mastering DALL·E 2: A Breakthrough in AI Art Generation](https:\u002F\u002Flearnopencv.com\u002Fmastering-dall-e-2\u002F)| |\n|[Top 10 AI Art Generation Tools using Diffusion Models](https:\u002F\u002Flearnopencv.com\u002Fai-art-generation-tools\u002F)| |\n|[The Future of Image Recognition is Here: PyTorch Vision Transformer](https:\u002F\u002Flearnopencv.com\u002Fthe-future-of-image-recognition-is-here-pytorch-vision-transformer\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVision_Transformer_PyTorch)|\n|[Understanding Attention Mechanism in Transformer Neural Networks](https:\u002F\u002Flearnopencv.com\u002Fattention-mechanism-in-transformer-neural-networks\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAttention_Mechanism_Introduction)|\n| [Deploying a Deep Learning Model using Hugging Face Spaces and Gradio](https:\u002F\u002Flearnopencv.com\u002Fdeploy-deep-learning-model-huggingface-spaces\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDeploying-a-Deep-Learning-Model-using-Hugging-Face-Spaces-and-Gradio) |\n| [Train YOLOv8 on Custom Dataset – A Complete Tutorial](https:\u002F\u002Flearnopencv.com\u002Ftrain-yolov8-on-custom-dataset\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTrain-YOLOv8-on-Custom-Dataset-A-Complete-Tutorial) |\n| [Introduction to Diffusion Models for Image Generation](https:\u002F\u002Flearnopencv.com\u002Fimage-generation-using-diffusion-models\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntroduction-to-Diffusion-Models-for-Image-Generation) |\n| [Building An Automated Image Annotation Tool: PyOpenAnnotate](https:\u002F\u002Flearnopencv.com\u002Fbuilding-automated-image-annotation-tool-pyopenannotate\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FBuilding-An-Automated-Image-Annotation-Tool-PyOpenAnnotate\u002F) |\n| [Ultralytics YOLOv8: State-of-the-Art YOLO Models](https:\u002F\u002Flearnopencv.com\u002Fultralytics-yolov8\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FUltralytics-YOLOv8-State-of-the-Art-YOLO-Models) |\n| [Getting Started with YOLOv5 Instance Segmentation](https:\u002F\u002Flearnopencv.com\u002Fgetting-started-with-yolov5-instance-segmentation\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FGetting-Started-with-YOLOv5-Instance-Segmentation) |\n|[The Ultimate Guide To DeepLabv3 - With PyTorch Inference](https:\u002F\u002Flearnopencv.com\u002Fdeeplabv3-ultimate-guide\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FThe-ultimate-guide-to-deeplabv3)|\n|[AI Fitness Trainer using MediaPipe: Squats Analysis](https:\u002F\u002Flearnopencv.com\u002Fai-fitness-trainer-using-mediapipe\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAI-Fitness-Trainer-Using-MediaPipe-Analyzing-Squats)|\n|[YoloR - Paper Explanation & Inference -An In-Depth Analysis](https:\u002F\u002Flearnopencv.com\u002Fyolor-paper-explanation-inference-an-in-depth-analysis\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYoloR-paper-explanation-analysis)|\n|[Roadmap To an Automated Image Annotation Tool Using Python](https:\u002F\u002Flearnopencv.com\u002Fautomated-image-annotation-tool-using-opencv-python\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FRoadmap-To-an-Automated-Image-Annotation-Tool-Using-Python)|\n|[Performance Comparison of YOLO Object Detection Models – An Intensive Study](https:\u002F\u002Flearnopencv.com\u002Fperformance-comparison-of-yolo-models\u002F)||\n|[FCOS - Anchor Free Object Detection Explained](https:\u002F\u002Flearnopencv.com\u002Ffcos-anchor-free-object-detection-explained\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFCOS-Inference-using-PyTorch)|\n| [YOLOv6 Custom Dataset Training – Underwater Trash Detection](https:\u002F\u002Flearnopencv.com\u002Fyolov6-custom-dataset-training\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv6-Custom-Dataset-Training-Underwater-Trash-Detection) |\n|[What is EXIF Data in Images?](https:\u002F\u002Fwww.learnopencv.com\u002Fwhat-is-exif-data-in-images\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FWhat-is-EXIF-Data-in-Images)|\n|[t-SNE: T-Distributed Stochastic Neighbor Embedding Explained](https:\u002F\u002Flearnopencv.com\u002Ft-sne-t-distributed-stochastic-neighbor-embedding-explained\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Ft-SNE-with-Tensorboard)|\n|[CenterNet: Objects as Points – Anchor-free Object Detection Explained](https:\u002F\u002Flearnopencv.com\u002Fcenternet-anchor-free-object-detection-explained\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fcenternet-with-tf-hub)|\n|[YOLOv7 Pose vs MediaPipe in Human Pose Estimation](https:\u002F\u002Flearnopencv.com\u002Fyolov7-pose-vs-mediapipe-in-human-pose-estimation\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv7-Pose-vs-MediaPipe-in-Human-Pose-Estimation)|\n|[YOLOv6 Object Detection – Paper Explanation and Inference](https:\u002F\u002Flearnopencv.com\u002Fyolov6-object-detection\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv6-Object-Detection-Paper-Explanation-and-Inference)|\n|[YOLOX Object Detector Paper Explanation and Custom Training](https:\u002F\u002Flearnopencv.com\u002Fyolox-object-detector-paper-explanation-and-custom-training\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOX-Object-Detection-Paper-Explanation-and-Custom-Training)|\n|[Driver Drowsiness Detection Using Mediapipe In Python](https:\u002F\u002Flearnopencv.com\u002Fdriver-drowsiness-detection-using-mediapipe-in-python\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDriver-Drowsiness-detection-using-Mediapipe-in-Python)|\n|[GTC 2022 Big Bang AI announcements: Everything you need to know](https:\u002F\u002Flearnopencv.com\u002Fgtc-2022-big-bang-ai-announcements-everything-you-need-to-know\u002F)||\n|[NVIDIA GTC 2022 : The most important AI event this Fall](https:\u002F\u002Flearnopencv.com\u002Fnvidia-gtc-2022-the-most-important-ai-event-this-fall\u002F)||\n|[Object Tracking and Reidentification with FairMOT](https:\u002F\u002Flearnopencv.com\u002Fobject-tracking-and-reidentification-with-fairmot\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FObject-Tracking-and-Reidentification-with-FairMOT) |\n|[What is Face Detection? – The Ultimate Guide for 2022](https:\u002F\u002Flearnopencv.com\u002Fwhat-is-face-detection-the-ultimate-guide\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFace-Detection-Ultimate-Guide) |\n|[Document Scanner: Custom Semantic Segmentation using PyTorch-DeepLabV3](https:\u002F\u002Flearnopencv.com\u002Fcustom-document-segmentation-using-deep-learning\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDocument-Scanner-Custom-Semantic-Segmentation-using-PyTorch-DeepLabV3)|\n|[Fine Tuning YOLOv7 on Custom Dataset](https:\u002F\u002Flearnopencv.com\u002Ffine-tuning-yolov7-on-custom-dataset\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFine-Tuning-YOLOv7)|\n|[Center Stage for Zoom Calls using MediaPipe](https:\u002F\u002Flearnopencv.com\u002FCenter-Stage-for-zoom-call-using-mediapipe\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FCenterStage)|\n|[Mean Average Precision (mAP) in Object Detection](https:\u002F\u002Flearnopencv.com\u002Fmean-average-precision-map-object-detection-model-evaluation-metric\u002F)||\n|[YOLOv7 Object Detection Paper Explanation and Inference](https:\u002F\u002Flearnopencv.com\u002Fyolov7-object-detection-paper-explanation-and-inference\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv7-Object-Detection-Paper-Explanation-and-Inference)|\n|[Pothole Detection using YOLOv4 and Darknet](https:\u002F\u002Flearnopencv.com\u002Fpothole-detection-using-yolov4-and-darknet\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPothole-Detection-using-YOLOv4-and-Darknet)|\n|[Automatic Document Scanner using OpenCV](https:\u002F\u002Flearnopencv.com\u002Fautomatic-document-scanner-using-opencv\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAutomatic-Document-Scanner)|\n|[Demystifying GPU architectures for deep learning: Part 2](https:\u002F\u002Flearnopencv.com\u002Fdemystifying-gpu-architectures-for-deep-learning-part-2\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fgpu_arch_and_CUDA)|\n|[Demystifying GPU Architectures For Deep Learning](https:\u002F\u002Flearnopencv.com\u002Fdemystifying-gpu-architectures-for-deep-learning\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fgpu_arch_and_CUDA)|\n|[Intersection-over-Union(IoU)-in-Object-Detection-and-Segmentation](https:\u002F\u002Flearnopencv.com\u002Fintersection-over-unioniou-in-object-detection-and-segmentation\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntersection-over-Union-IoU-in-Object-Detection-and-Segmentation)|\n|[Understanding Multiple Object Tracking using DeepSORT](https:\u002F\u002Flearnopencv.com\u002Funderstanding-multiple-object-tracking-using-deepsort\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FUnderstanding-Multiple-Object-Tracking-using-DeepSORT)|\n|[Optical Character Recognition using PaddleOCR](https:\u002F\u002Flearnopencv.com\u002Foptical-character-recognition-using-paddleocr\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOptical-Character-Recognition-using-PaddleOCR)|\n|[Gesture Control in Zoom Call using Mediapipe](https:\u002F\u002Flearnopencv.com\u002Fgesture-control-in-zoom-call-using-mediapipe\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fzoom-gestures)|\n|[A Deep Dive into Tensorflow Model Optimization](https:\u002F\u002Flearnopencv.com\u002Fdeep-dive-into-tensorflow-model-optimization-toolkit\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FA-Deep-Dive-into-Tensorflow-Model-Optimization)|\n|[DepthAI Pipeline Overview: Creating a Complex Pipeline](https:\u002F\u002Flearnopencv.com\u002Fdepthai-pipeline-overview-creating-a-complex-pipeline\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOAK-DepthAi-Pipeline-Overview)|\n|[TensorFlow Lite Model Maker: Create Models for On-Device Machine Learning](https:\u002F\u002Flearnopencv.com\u002Ftensorflow-lite-model-maker-create-models-for-on-device-machine-learning\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTensorflow-Lite-Model-Maker-Create-Models-for-On-Device-ML)|\n|[TensorFlow Lite: Model Optimization for On Device Machine Learning](https:\u002F\u002Flearnopencv.com\u002Ftensorflow-lite-model-optimization-for-on-device-machine-learning)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTensorFlow-Lite-Model-Optimization-for-On-Device-MachineLearning)|\n|[Object detection with depth measurement using pre-trained models with OAK-D](https:\u002F\u002Flearnopencv.com\u002Fobject-detection-with-depth-measurement-with-oak-d\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOAK-Object-Detection-with-Depth)|\n|[Custom Object Detection Training using YOLOv5](https:\u002F\u002Flearnopencv.com\u002Fcustom-object-detection-training-using-yolov5\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FCustom-Object-Detection-Training-using-YOLOv5)|\n|[Object Detection using Yolov5 and OpenCV DNN (C++\u002FPython)](https:\u002F\u002Flearnopencv.com\u002Fobject-detection-using-yolov5-and-opencv-dnn-in-c-and-python\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FObject-Detection-using-YOLOv5-and-OpenCV-DNN-in-CPP-and-Python)|\n|[Create Snapchat\u002FInstagram filters using Mediapipe](https:\u002F\u002Flearnopencv.com\u002Fcreate-snapchat-instagram-filters-using-mediapipe\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FCreate-AR-filters-using-Mediapipe)|\n|[AUTOSAR C++ compliant deep learning inference with TensorRT](https:\u002F\u002Flearnopencv.com\u002Fautosar-c-compliant-deep-learning-inference-with-tensorrt\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Findustrial_cv_TensorRT_cpp)|\n|[NVIDIA GTC 2022 Day 4 Highlights: Meet the new Jetson Orin](https:\u002F\u002Flearnopencv.com\u002Fnvidia-gtc-2022-day-4-highlights-meet-the-new-jetson-orin\u002F)||\n|[NVIDIA GTC 2022 Day 3 Highlights: Deep Dive into Hopper architecture](https:\u002F\u002Flearnopencv.com\u002Fnvidia-gtc-2022-day-3-highlights-deep-dive-into-hopper-architecture\u002F)||\n|[NVIDIA GTC 2022 Day 2 Highlights: Jensen’s Keynote](https:\u002F\u002Flearnopencv.com\u002Fnvidia-gtc-2022-day-2-highlights\u002F)||\n|[NVIDIA GTC 2022 Day 1 Highlights: Brilliant Start](https:\u002F\u002Flearnopencv.com\u002Fgtc-day-1-highlights\u002F)||\n|[Automatic License Plate Recognition using Python](https:\u002F\u002Flearnopencv.com\u002Fautomatic-license-plate-recognition-using-deep-learning\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FALPR)|\n|[Building a Poor Body Posture Detection and Alert System using MediaPipe](https:\u002F\u002Flearnopencv.com\u002Fbuilding-a-body-posture-analysis-system-using-mediapipe\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPosture-analysis-system-using-MediaPipe-Pose)|\n|[Introduction to MediaPipe](https:\u002F\u002Flearnopencv.com\u002Fintroduction-to-mediapipe\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntroduction-to-MediaPipe)|\n|[Disparity Estimation using Deep Learning](https:\u002F\u002Flearnopencv.com\u002Fdisparity-estimation-using-deep-learning\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDisparity-Estimation-Using-Deep-Learning)|\n|[How to build Chrome Dino game bot using OpenCV Feature Matching](https:\u002F\u002Flearnopencv.com\u002Fhow-to-build-chrome-dino-game-bot-using-opencv-feature-matching\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FChrome-Dino-Bot-using-OpenCV-feature-matching)|\n|[Top 10 Sources to Find Computer Vision and AI Models](https:\u002F\u002Flearnopencv.com\u002Ftop-10-sources-to-find-computer-vision-and-ai-models\u002F)||\n|[Multi-Attribute and Graph-based Object Detection](https:\u002F\u002Flearnopencv.com\u002Fmulti-attribute-and-graph-based-object-detection\u002F)||\n|[Plastic Waste Detection with Deep Learning](https:\u002F\u002Flearnopencv.com\u002Fplastic-waste-detection-with-deep-learning\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPlastic-Waste-Detection-with-Deep-Learning)|\n|[Ensemble Deep Learning-based Defect Classification and Detection in SEM Images](https:\u002F\u002Flearnopencv.com\u002Fensemble-deep-learning-based-defect-classification-and-detection-in-sem-images\u002F)||\n|[Building Industrial embedded deep learning inference pipelines with TensorRT](https:\u002F\u002Flearnopencv.com\u002Fbuilding-industrial-embedded-deep-learning-inference-pipelines-with-tensorrt\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Findustrial_cv_TensorRT_python)|\n|[Transfer Learning for Medical Images](https:\u002F\u002Flearnopencv.com\u002Ftransfer-learning-for-medical-images\u002F)||\n|[Stereo Vision and Depth Estimation using OpenCV AI Kit](https:\u002F\u002Flearnopencv.com\u002Fstereo-vision-and-depth-estimation-using-opencv-ai-kit\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Foak-getting-started)|\n|[Introduction to OpenCV AI Kit and DepthAI](https:\u002F\u002Flearnopencv.com\u002Fintroduction-to-opencv-ai-kit-and-depthai\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Foak-getting-started)|\n|[WeChat QR Code Scanner in OpenCV](https:\u002F\u002Flearnopencv.com\u002Fwechat-qr-code-scanner-in-opencv)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FWeChat-QRCode-Scanner-OpenCV)|\n|[AI behind the Diwali 2021 ‘Not just a Cadbury ad’](https:\u002F\u002Flearnopencv.com\u002Fai-behind-the-diwali-2021-not-just-a-cadbury-ad\u002F)| |\n|[Model Selection and Benchmarking with Modelplace.AI](https:\u002F\u002Flearnopencv.com\u002Fmodel-selection-and-benchmarking-with-modelplace-ai\u002F)|[Model Zoo](https:\u002F\u002Fmodelplace.ai\u002F)|\n|[Real-time style transfer in a zoom meeting](https:\u002F\u002Flearnopencv.com\u002Freal-time-style-transfer-in-a-zoom-meeting\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fstyle-transfer-zoom)|\n| [Introduction to OpenVino Deep Learning Workbench](https:\u002F\u002Flearnopencv.com\u002Fintroduction-to-openvino-deep-learning-workbench\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntroduction-to-OpenVino-Deep-Learning-Workbench) |\n| [Running OpenVino Models on Intel Integrated GPU](https:\u002F\u002Flearnopencv.com\u002Frunning-openvino-models-on-intel-integrated-gpu\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FRunning-OpenVino-Models-on-Intel-Integrated-GPU) |\n|[Post Training Quantization with OpenVino Toolkit](https:\u002F\u002Flearnopencv.com\u002Fpost-training-quantization-with-openvino-toolkit\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPost-Training-Quantization-with-OpenVino-Toolkit)|\n|[Introduction to Intel OpenVINO Toolkit](https:\u002F\u002Flearnopencv.com\u002Fintroduction-to-intel-openvino-toolkit\u002F)||\n|[Human Action Recognition using Detectron2 and LSTM](https:\u002F\u002Flearnopencv.com\u002Fhuman-action-recognition-using-detectron2-and-lstm\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHuman-Action-Recognition-Using-Detectron2-And-Lstm)|\n|[Pix2Pix:Image-to-Image Translation in PyTorch & TensorFlow](https:\u002F\u002Flearnopencv.com\u002Fpaired-image-to-image-translation-pix2pix\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImage-to-Image-Translation-with-GAN)|\n|[Conditional GAN (cGAN) in PyTorch and TensorFlow](https:\u002F\u002Flearnopencv.com\u002Fconditional-gan-cgan-in-pytorch-and-tensorflow\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FConditional-GAN-PyTorch-TensorFlow)|\n|[Deep Convolutional GAN in PyTorch and TensorFlow](https:\u002F\u002Flearnopencv.com\u002Fdeep-convolutional-gan-in-pytorch-and-tensorflow\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDeep-Convolutional-GAN)|\n|[Introduction to Generative Adversarial Networks (GANs)](https:\u002F\u002Flearnopencv.com\u002Fintroduction-to-generative-adversarial-networks\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FIntro-to-Generative-Adversarial-Network)|\n|[Human Pose Estimation using Keypoint RCNN in PyTorch](https:\u002F\u002Flearnopencv.com\u002Fhuman-pose-estimation-using-keypoint-rcnn-in-pytorch\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-Keypoint-RCNN)|\n|[Non Maximum Suppression: Theory and Implementation in PyTorch](https:\u002F\u002Flearnopencv.com\u002Fnon-maximum-suppression-theory-and-implementation-in-pytorch)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FNon-Maximum-Suppression)|\n|[MRNet – The Multi-Task Approach](https:\u002F\u002Flearnopencv.com\u002Fmrnet-multitask-approach\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMRnet-MultiTask-Approach) |\n|[Generative and Discriminative Models](https:\u002F\u002Flearnopencv.com\u002Fgenerative-and-discriminative-models\u002F)| |\n|[Playing Chrome's T-Rex Game with Facial Gestures](https:\u002F\u002Flearnopencv.com\u002Fplaying-chromes-t-rex-game-with-facial-gestures\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPlaying-Chrome-TRex-Game-with-Facial-Gestures) |\n|[Variational Autoencoder in TensorFlow](https:\u002F\u002Flearnopencv.com\u002Fvariational-autoencoder-in-tensorflow\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVariational-Autoencoder-TensorFlow) |\n|[Autoencoder in TensorFlow 2: Beginner’s Guide](https:\u002F\u002Flearnopencv.com\u002Fautoencoder-in-tensorflow-2-beginners-guide\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAutoencoder-in-TensorFlow) |\n|[Deep Learning with OpenCV DNN Module: A Definitive Guide](https:\u002F\u002Flearnopencv.com\u002Fdeep-learning-with-opencvs-dnn-module-a-definitive-guide\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDeep-Learning-with-OpenCV-DNN-Module) |\n|[Depth perception using stereo camera (Python\u002FC++)](https:\u002F\u002Flearnopencv.com\u002Fdepth-perception-using-stereo-camera-python-c\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDepth-Perception-Using-Stereo-Camera) |\n|[Contour Detection using OpenCV (Python\u002FC++)](https:\u002F\u002Flearnopencv.com\u002Fcontour-detection-using-opencv-python-c\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FContour-Detection-using-OpenCV) |\n|[Super Resolution in OpenCV](https:\u002F\u002Flearnopencv.com\u002Fsuper-resolution-in-opencv\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FSuper-Resolution-in-OpenCV) |\n|[Improving Illumination in Night Time Images](https:\u002F\u002Flearnopencv.com\u002Fimproving-illumination-in-night-time-images\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImproving-Illumination-in-Night-Time-Images) |\n|[Video Classification and Human Activity Recognition](https:\u002F\u002Flearnopencv.com\u002Fintroduction-to-video-classification-and-human-activity-recognition\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fvideo-classification-and-human-activity-recognition) |\n|[How to use OpenCV DNN Module with Nvidia GPU on Windows](https:\u002F\u002Flearnopencv.com\u002Fhow-to-use-opencv-dnn-module-with-nvidia-gpu-on-windows) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOpenCV-dnn-gpu-support-Windows) |\n|[How to use OpenCV DNN Module with NVIDIA GPUs](https:\u002F\u002Flearnopencv.com\u002Fopencv-dnn-with-gpu-support\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOpenCV-dnn-gpu-support-Linux) |\n|[Code OpenCV in Visual Studio](https:\u002F\u002Flearnopencv.com\u002Fcode-opencv-in-visual-studio\u002F) | |\n|[Install OpenCV on Windows – C++ \u002F Python](https:\u002F\u002Flearnopencv.com\u002Finstall-opencv-on-windows\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FInstall-OpenCV-Windows-exe) |\n|[Face Recognition with ArcFace](https:\u002F\u002Fwww.learnopencv.com\u002Fface-recognition-with-arcface\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFace-Recognition-with-ArcFace)|\n|[Background Subtraction with OpenCV and BGS Libraries](https:\u002F\u002Fwww.learnopencv.com\u002Fbackground-subtraction-with-opencv-and-bgs-libraries\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FBackground-Subtraction) |\n|[RAFT: Optical Flow estimation using Deep Learning](https:\u002F\u002Flearnopencv.com\u002Foptical-flow-using-deep-learning-raft\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOptical-Flow-Estimation-using-Deep-Learning-RAFT)|\n|[Making A Low-Cost Stereo Camera Using OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fmaking-a-low-cost-stereo-camera-using-opencv\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fstereo-camera)|\n|[Optical Flow in OpenCV (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Foptical-flow-in-opencv)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOptical-Flow-in-OpenCV)|\n|[Introduction to Epipolar Geometry and Stereo Vision](https:\u002F\u002Fwww.learnopencv.com\u002Fintroduction-to-epipolar-geometry-and-stereo-vision\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FEpipolarGeometryAndStereoVision)|\n|[Classification With Localization: Convert any keras Classifier to a Detector](https:\u002F\u002Fwww.learnopencv.com\u002Fclassification-with-localization\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FClassification-with-localization-convert-any-keras-classifier-into-a-detector\u002FREADME.md) |\n|[Photoshop Filters in OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fphotoshop-filters-in-opencv\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPhotoshop-Filters-in-OpenCV)|\n|[Tetris Game using OpenCV Python](https:\u002F\u002Fwww.learnopencv.com\u002Ftetris-with-opencv-python)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTetris)|\n|[Image Classification with OpenCV for Android](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-classification-with-opencv-for-android\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDNN-OpenCV-Classification-Android) |\n|[Image Classification with OpenCV Java](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-classification-with-opencv-java)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDNN-OpenCV-Classification-with-Java) |\n|[PyTorch to Tensorflow Model Conversion](https:\u002F\u002Fwww.learnopencv.com\u002Fpytorch-to-tensorflow-model-conversion\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-to-TensorFlow-Model-Conversion) |\n|[Snake Game with OpenCV Python](https:\u002F\u002Fwww.learnopencv.com\u002Fsnake-game-with-opencv-python\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSnakeGame) |\n|[Stanford MRNet Challenge: Classifying Knee MRIs](https:\u002F\u002Fwww.learnopencv.com\u002Fstanford-mrnet-challenge-classifying-knee-mris\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMRNet-Single-Model) |\n|[Experiment Logging with TensorBoard and wandb](https:\u002F\u002Fwww.learnopencv.com\u002Fexperiment-logging-with-tensorboard-and-wandb)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-Vision-Experiment-Logging) |\n|[Understanding Lens Distortion](https:\u002F\u002Fwww.learnopencv.com\u002Funderstanding-lens-distortion\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FUnderstandingLensDistortion) |\n|[Image Matting with state-of-the-art Method “F, B, Alpha Matting”](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-matting-with-state-of-the-art-method-f-b-alpha-matting\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFBAMatting) |\n|[Bag Of Tricks For Image Classification - Let's check if it is working or not](https:\u002F\u002Fwww.learnopencv.com\u002Fbag-of-tricks-for-image-classification-lets-check-if-it-is-working-or-not\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FBag-Of-Tricks-For-Image-Classification) |\n|[Getting Started with OpenCV CUDA Module](https:\u002F\u002Fwww.learnopencv.com\u002Fgetting-started-opencv-cuda-module\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FGetting-Started-OpenCV-CUDA-Module) |\n|[Training a Custom Object Detector with DLIB & Making Gesture Controlled Applications](https:\u002F\u002Fwww.learnopencv.com\u002Ftraining-a-custom-object-detector-with-dlib-making-gesture-controlled-applications\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTraining_a_custom_hand_detector_with_dlib) |\n|[How To Run Inference Using TensorRT C++ API](https:\u002F\u002Fwww.learnopencv.com\u002Fhow-to-run-inference-using-tensorrt-c-api\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-ONNX-TensorRT-CPP) |\n|[Using Facial Landmarks for Overlaying Faces with Medical Masks](https:\u002F\u002Fwww.learnopencv.com\u002Fusing-facial-landmarks-for-overlaying-faces-with-masks\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFaceMaskOverlay) |\n|[Tensorboard with PyTorch Lightning](https:\u002F\u002Fwww.learnopencv.com\u002Ftensorboard-with-pytorch-lightning)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTensorBoard-With-Pytorch-Lightning) |\n|[Otsu's Thresholding with OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fotsu-thresholding-with-opencv\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fotsu-method) |\n|[PyTorch-to-CoreML-model-conversion](https:\u002F\u002Fwww.learnopencv.com\u002Fpytorch-to-coreml-model-conversion\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-to-CoreML-model-conversion) |\n|[Playing Rock, Paper, Scissors with AI](https:\u002F\u002Fwww.learnopencv.com\u002Fplaying-rock-paper-scissors-with-ai\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPlaying-rock-paper-scissors-with-AI) |\n|[CNN Receptive Field Computation Using Backprop with TensorFlow](https:\u002F\u002Fwww.learnopencv.com\u002Fcnn-receptive-field-computation-using-backprop-with-tensorflow\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTensorFlow-Receptive-Field-With-Backprop)|\n|[CNN Fully Convolutional Image Classification with TensorFlow](https:\u002F\u002Fwww.learnopencv.com\u002Fcnn-fully-convolutional-image-classification-with-tensorflow) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTensorFlow-Fully-Convolutional-Image-Classification) |\n|[How to convert a model from PyTorch to TensorRT and speed up inference](https:\u002F\u002Fwww.learnopencv.com\u002Fhow-to-convert-a-model-from-pytorch-to-tensorrt-and-speed-up-inference\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-ONNX-TensorRT) |\n|[Efficient image loading](https:\u002F\u002Fwww.learnopencv.com\u002Fefficient-image-loading\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FEfficient-image-loading) |\n|[Graph Convolutional Networks: Model Relations In Data](https:\u002F\u002Fwww.learnopencv.com\u002Fgraph-convolutional-networks-model-relations-in-data\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FGraph-Convolutional-Networks-Model-Relations-In-Data)|\n|[Getting Started with Federated Learning with PyTorch and PySyft](https:\u002F\u002Fwww.learnopencv.com\u002Ffederated-learning-using-pytorch-and-pysyft\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFederated-Learning-Intro)|\n|[Creating a Virtual Pen & Eraser](http:\u002F\u002Fwww.learnopencv.com\u002Fcreating-a-virtual-pen-and-eraser-with-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FCreating-a-Virtual-Pen-and-Eraser) |\n|[Getting Started with PyTorch Lightning](https:\u002F\u002Fwww.learnopencv.com\u002Fgetting-started-with-pytorch-lightning\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPytorch-Lightning)|\n|[Multi-Label Image Classification with PyTorch: Image Tagging](https:\u002F\u002Fwww.learnopencv.com\u002Fmulti-label-image-classification-with-pytorch-image-tagging\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-Multi-Label-Image-Classification-Image-Tagging)|\n|[Funny Mirrors Using OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002FFunny-Mirrors-Using-OpenCV\u002F)|[code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFunnyMirrors)|\n|[t-SNE for ResNet feature visualization](https:\u002F\u002Fwww.learnopencv.com\u002Ft-sne-for-feature-visualization\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTSNE)|\n|[Multi-Label Image Classification with Pytorch](https:\u002F\u002Fwww.learnopencv.com\u002Fmulti-label-image-classification-with-pytorch\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-Multi-Label-Image-Classification)|\n|[CNN Receptive Field Computation Using Backprop](https:\u002F\u002Fwww.learnopencv.com\u002Fcnn-receptive-field-computation-using-backprop\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-Receptive-Field-With-Backprop)|\n|[CNN Receptive Field Computation Using Backprop with TensorFlow](https:\u002F\u002Fwww.learnopencv.com\u002Fcnn-receptive-field-computation-using-backprop-with-tensorflow\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTensorFlow-Receptive-Field-With-Backprop)|\n|[Augmented Reality using AruCo Markers in OpenCV(C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Faugmented-reality-using-aruco-markers-in-opencv-(c++-python)\u002F) |[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAugmentedRealityWithArucoMarkers)|\n|[Fully Convolutional Image Classification on Arbitrary Sized Image](https:\u002F\u002Fwww.learnopencv.com\u002Ffully-convolutional-image-classification-on-arbitrary-sized-image\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-Fully-Convolutional-Image-Classification)|\n|[Camera Calibration using OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fcamera-calibration-using-opencv\u002F) |[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FCameraCalibration)|\n|[Geometry of Image Formation](https:\u002F\u002Fwww.learnopencv.com\u002Fgeometry-of-image-formation\u002F) ||\n|[Ensuring Training Reproducibility in Pytorch](https:\u002F\u002Fwww.learnopencv.com\u002Fensuring-training-reproducibility-in-pytorch) ||\n|[Gaze Tracking](https:\u002F\u002Fwww.learnopencv.com\u002Fgaze-tracking\u002F) ||\n|[Simple Background Estimation in Videos Using OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fsimple-background-estimation-in-videos-using-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVideoBackgroundEstimation)|\n|[Applications of Foreground-Background separation with Semantic Segmentation](https:\u002F\u002Fwww.learnopencv.com\u002Fapplications-of-foreground-background-separation-with-semantic-segmentation\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fapp-seperation-semseg) |\n|[EfficientNet: Theory + Code](https:\u002F\u002Fwww.learnopencv.com\u002Fefficientnet-theory-code) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FEfficientNet) |\n|[PyTorch for Beginners: Mask R-CNN Instance Segmentation with PyTorch](https:\u002F\u002Fwww.learnopencv.com\u002Fmask-r-cnn-instance-segmentation-with-pytorch\u002F) | [Code](.\u002FPyTorch-Mask-RCNN) |\n|[PyTorch for Beginners: Faster R-CNN Object Detection with PyTorch](https:\u002F\u002Fwww.learnopencv.com\u002Ffaster-r-cnn-object-detection-with-pytorch) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-faster-RCNN) |\n|[PyTorch for Beginners: Semantic Segmentation using torchvision](https:\u002F\u002Fwww.learnopencv.com\u002Fpytorch-for-beginners-semantic-segmentation-using-torchvision\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-Segmentation-torchvision) |\n|[PyTorch for Beginners: Comparison of pre-trained models for Image Classification](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-classification-using-pre-trained-models-using-pytorch\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImage-classification-pre-trained-models\u002FImage_Classification_using_pre_trained_models.ipynb) |\n|[PyTorch for Beginners: Basics](https:\u002F\u002Fwww.learnopencv.com\u002Fpytorch-for-beginners-basics\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FPyTorch-for-Beginners\u002FPyTorch_for_Beginners.ipynb) |\n|[PyTorch Model Inference using ONNX and Caffe2](https:\u002F\u002Fwww.learnopencv.com\u002Fpytorch-model-inference-using-onnx-and-caffe2\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FInference-for-PyTorch-Models\u002FONNX-Caffe2) |\n|[Image Classification Using Transfer Learning in PyTorch](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-classification-using-transfer-learning-in-pytorch\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImage-Classification-in-PyTorch) |\n|[Hangman: Creating games in OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fhangman-creating-games-in-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHangman) |\n|[Image Inpainting with OpenCV (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-inpainting-with-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImage-Inpainting) |\n|[Hough Transform with OpenCV (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Fhough-transform-with-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHough-Transform) |\n|[Xeus-Cling: Run C++ code in Jupyter Notebook](https:\u002F\u002Fwww.learnopencv.com\u002Fxeus-cling-run-c-code-in-jupyter-notebook\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FXeusCling) |\n|[Gender & Age Classification using OpenCV Deep Learning ( C++\u002FPython )](https:\u002F\u002Fwww.learnopencv.com\u002Fage-gender-classification-using-opencv-deep-learning-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAgeGender) |\n|[Invisibility Cloak using Color Detection and Segmentation with OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Finvisibility-cloak-using-color-detection-and-segmentation-with-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FInvisibilityCloak) |\n|[Fast Image Downloader for Open Images V4 (Python)](https:\u002F\u002Fwww.learnopencv.com\u002Ffast-image-downloader-for-open-images-v4\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FdownloadOpenImages) |\n|[Deep Learning based Text Detection Using OpenCV (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-based-text-detection-using-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FTextDetectionEAST) |\n|[Video Stabilization Using Point Feature Matching in OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fvideo-stabilization-using-point-feature-matching-in-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVideoStabilization) |\n|[Training YOLOv3 : Deep Learning based Custom Object Detector](https:\u002F\u002Fwww.learnopencv.com\u002Ftraining-yolov3-deep-learning-based-custom-object-detector\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FYOLOv3-Training-Snowman-Detector ) |\n|[Using OpenVINO with OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fusing-openvino-with-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOpenVINO-OpenCV) |\n|[Duplicate Search on Quora Dataset](https:\u002F\u002Fwww.learnopencv.com\u002Fduplicate-search-on-quora-dataset\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FQuora-Dataset-Duplicate-Search) |\n|[Shape Matching using Hu Moments (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Fshape-matching-using-hu-moments-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHuMoments) |\n|[Install OpenCV 4 on CentOS (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-4-on-centos-7\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-3-on-centos.sh) |\n|[Install OpenCV 3.4.4 on CentOS (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-3-4-4-on-centos-7\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-3-on-centos.sh) |\n|[Install OpenCV 3.4.4 on Red Hat (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-3-4-4-on-red-hat\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-3-on-red-hat.sh) |\n|[Install OpenCV 4 on Red Hat (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-4-on-red-hat\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-4-on-red-hat.sh) |\n|[Install OpenCV 4 on macOS (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-4-on-macos\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-4-macos.sh) |\n|[Install OpenCV 3.4.4 on Raspberry Pi](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-3-4-4-on-raspberry-pi\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-3-raspberry-pi.sh) |\n|[Install OpenCV 3.4.4 on macOS (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-3-4-4-on-macos\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-3-macos.sh) |\n|[OpenCV QR Code Scanner (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Fopencv-qr-code-scanner-c-and-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FQRCode-OpenCV) |\n|[Install OpenCV 3.4.4 on Windows (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-3-4-4-on-windows\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FInstallScripts\u002FWindows-3) |\n|[Install OpenCV 3.4.4 on Ubuntu 16.04 (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-3-4-4-on-ubuntu-16-04\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-3-on-Ubuntu-16-04.sh) |\n|[Install OpenCV 3.4.4 on Ubuntu 18.04 (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-3-4-4-on-ubuntu-18-04\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-3-on-Ubuntu-18-04.sh) |\n|[Universal Sentence Encoder](https:\u002F\u002Fwww.learnopencv.com\u002Funiversal-sentence-encoder) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FUniversal-Sentence-Encoder) |\n|[Install OpenCV 4 on Raspberry Pi](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-4-on-raspberry-pi\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-4-raspberry-pi.sh) |\n|[Install OpenCV 4 on Windows (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-4-on-windows\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FInstallScripts\u002FWindows-4) |\n|[Face Detection – Dlib, OpenCV, and Deep Learning ( C++ \u002F Python )](https:\u002F\u002Flearnopencv.com\u002Fface-detection-opencv-dlib-and-deep-learning-c-python\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFaceDetectionComparison)|\n|[Hand Keypoint Detection using Deep Learning and OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fhand-keypoint-detection-using-deep-learning-and-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHandPose)|\n|[Deep learning based Object Detection and Instance Segmentation using Mask R-CNN in OpenCV (Python \u002F C++)](https:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-based-object-detection-and-instance-segmentation-using-mask-r-cnn-in-opencv-python-c\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMask-RCNN) |\n|[Install OpenCV 4 on Ubuntu 18.04 (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-4-on-ubuntu-18-04\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-4-on-Ubuntu-18-04.sh) |\n|[Install OpenCV 4 on Ubuntu 16.04 (C++ and Python)](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-4-on-ubuntu-16-04\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fblob\u002Fmaster\u002FInstallScripts\u002FinstallOpenCV-4-on-Ubuntu-16-04.sh) |\n|[Multi-Person Pose Estimation in OpenCV using OpenPose](https:\u002F\u002Fwww.learnopencv.com\u002Fmulti-person-pose-estimation-in-opencv-using-openpose\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOpenPose-Multi-Person) |\n|[Heatmap for Logo Detection using OpenCV (Python)](https:\u002F\u002Fwww.learnopencv.com\u002Fheatmap-for-logo-detection-using-opencv-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fheatmap)|\n|[Deep Learning based Object Detection using YOLOv3 with OpenCV ( Python \u002F C++ )](https:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-based-object-detection-using-yolov3-with-opencv-python-c\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FObjectDetection-YOLO)|\n|[Convex Hull using OpenCV in Python and C++](https:\u002F\u002Fwww.learnopencv.com\u002Fconvex-hull-using-opencv-in-python-and-c\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FConvexHull)|\n|[MultiTracker : Multiple Object Tracking using OpenCV (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Fmultitracker-multiple-object-tracking-using-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FMultiObjectTracker) |\n|[Convolutional Neural Network based Image Colorization using OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fconvolutional-neural-network-based-image-colorization-using-opencv\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FColorization)|\n|[SVM using scikit-learn](https:\u002F\u002Fwww.learnopencv.com\u002Fsvm-using-scikit-learn-in-python\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSVM-using-Python)|\n|[GOTURN: Deep Learning based Object Tracking](https:\u002F\u002Fwww.learnopencv.com\u002Fgoturn-deep-learning-based-object-tracking\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FGOTURN)|\n|[Find the Center of a Blob (Centroid) using OpenCV (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Ffind-center-of-blob-centroid-using-opencv-cpp-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FCenterofBlob)|\n|[Support Vector Machines (SVM)](https:\u002F\u002Fwww.learnopencv.com\u002Fsupport-vector-machines-svm\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSVM-using-Python)|\n|[Batch Normalization in Deep Networks](https:\u002F\u002Fwww.learnopencv.com\u002Fbatch-normalization-in-deep-networks\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FBatchNormalization)|\n|[Deep Learning based Character Classification using Synthetic Dataset](https:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-character-classification-using-synthetic-dataset\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FCharClassification)|\n|[Image Quality Assessment : BRISQUE](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-quality-assessment-brisque\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImageMetrics)|\n|[Understanding AlexNet](https:\u002F\u002Fwww.learnopencv.com\u002Funderstanding-alexnet\u002F)||\n|[Deep Learning based Text Recognition (OCR) using Tesseract and OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-based-text-recognition-ocr-using-tesseract-and-opencv\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOCR)|\n|[Deep Learning based Human Pose Estimation using OpenCV ( C++ \u002F Python )](https:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-based-human-pose-estimation-using-opencv-cpp-python\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FOpenPose)|\n|[Number of Parameters and Tensor Sizes in a Convolutional Neural Network (CNN)](https:\u002F\u002Fwww.learnopencv.com\u002Fnumber-of-parameters-and-tensor-sizes-in-convolutional-neural-network\u002F)| |\n|[How to convert your OpenCV C++ code into a Python module](https:\u002F\u002Fwww.learnopencv.com\u002Fhow-to-convert-your-opencv-c-code-into-a-python-module\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fpymodule)|\n|[CV4Faces : Best Project Award 2018](https:\u002F\u002Fwww.learnopencv.com\u002Fcv4faces-best-project-award-2018\u002F)| |\n|[Facemark : Facial Landmark Detection using OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Ffacemark-facial-landmark-detection-using-opencv\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFacialLandmarkDetection)|\n|[Image Alignment (Feature Based) using OpenCV (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-alignment-feature-based-using-opencv-c-python\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImageAlignment-FeatureBased)|\n|[Barcode and QR code Scanner using ZBar and OpenCV](https:\u002F\u002Fwww.learnopencv.com\u002Fbarcode-and-qr-code-scanner-using-zbar-and-opencv\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fbarcode-QRcodeScanner)|\n|[Keras Tutorial : Fine-tuning using pre-trained models](https:\u002F\u002Fwww.learnopencv.com\u002Fkeras-tutorial-fine-tuning-using-pre-trained-models\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FKeras-Fine-Tuning)|\n|[OpenCV Transparent API](https:\u002F\u002Fwww.learnopencv.com\u002Fopencv-transparent-api\u002F)| |\n|[Face Reconstruction using EigenFaces (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Fface-reconstruction-using-eigenfaces-cpp-python\u002F)|[Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FReconstructFaceUsingEigenFaces) |\n|[Eigenface using OpenCV (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Feigenface-using-opencv-c-python\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FEigenFace)|\n|[Principal Component Analysis](https:\u002F\u002Fwww.learnopencv.com\u002Fprincipal-component-analysis\u002F)| |\n|[Keras Tutorial : Transfer Learning using pre-trained models](https:\u002F\u002Fwww.learnopencv.com\u002Fkeras-tutorial-transfer-learning-using-pre-trained-models\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FKeras-Transfer-Learning) |\n|[Keras Tutorial : Using pre-trained Imagenet models](https:\u002F\u002Fwww.learnopencv.com\u002Fkeras-tutorial-using-pre-trained-imagenet-models\u002F)| [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FKeras-ImageNet-Models) |\n|[Technical Aspects of a Digital SLR](https:\u002F\u002Fwww.learnopencv.com\u002Ftechnical-aspects-of-a-digital-slr\u002F) | |\n|[Using Harry Potter interactive wand with OpenCV to create magic](https:\u002F\u002Fwww.learnopencv.com\u002Fusing-harry-potter-interactive-wand-with-opencv-to-create-magic\u002F)| |\n|[Install OpenCV 3 and Dlib on Windows ( Python only )](https:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-3-and-dlib-on-windows-python-only\u002F)| |\n|[Image Classification using Convolutional Neural Networks in Keras](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-classification-using-convolutional-neural-networks-in-keras)      | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FKerasCNN-CIFAR)|\n|[Understanding Autoencoders using Tensorflow (Python)](https:\u002F\u002Fwww.learnopencv.com\u002Funderstanding-autoencoders-using-tensorflow-python\u002F)      | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDenoisingAutoencoder)|\n|[Best Project Award : Computer Vision for Faces](https:\u002F\u002Fwww.learnopencv.com\u002Fbest-project-award-computer-vision-for-faces\u002F) | |\n|[Understanding Activation Functions in Deep Learning](https:\u002F\u002Fwww.learnopencv.com\u002Funderstanding-activation-functions-in-deep-learning\u002F)      | |\n|[Image Classification using Feedforward Neural Network in Keras](https:\u002F\u002Fwww.learnopencv.com\u002Fimage-classification-using-feedforward-neural-network-in-keras\u002F)      | [Code](https:\u002F\u002Fgithub.com\u002Fkromydas\u002Flearnopencv\u002Ftree\u002Fmaster\u002FKeras-MLP-MNIST-Classification)|\n|[Exposure Fusion using OpenCV (C++\u002FPython)](https:\u002F\u002Fwww.learnopencv.com\u002Fexposure-fusion-using-opencv-cpp-python\u002F)      | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FExposureFusion)|\n|[Understanding Feedforward Neural Networks](https:\u002F\u002Fwww.learnopencv.com\u002Funderstanding-feedforward-neural-networks\u002F)      | |\n|[High Dynamic Range (HDR) Imaging using OpenCV (C++\u002FPython)](http:\u002F\u002Fwww.learnopencv.com\u002Fhigh-dynamic-range-hdr-imaging-using-opencv-cpp-python)      | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fhdr)|\n|[Deep learning using Keras – The Basics](http:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-using-keras-the-basics)      | [Code](https:\u002F\u002Fgithub.com\u002Fkromydas\u002Flearnopencv\u002Ftree\u002Fmaster\u002FKeras-Linear-Regression)|\n|[Selective Search for Object Detection (C++ \u002F Python)](http:\u002F\u002Fwww.learnopencv.com\u002Fselective-search-for-object-detection-cpp-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSelectiveSearch) |\n|[Installing Deep Learning Frameworks on Ubuntu with CUDA support](http:\u002F\u002Fwww.learnopencv.com\u002Finstalling-deep-learning-frameworks-on-ubuntu-with-cuda-support\u002F) | |\n|[Parallel Pixel Access in OpenCV using forEach](http:\u002F\u002Fwww.learnopencv.com\u002Fparallel-pixel-access-in-opencv-using-foreach\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FforEach) |\n|[cvui: A GUI lib built on top of OpenCV drawing primitives](http:\u002F\u002Fwww.learnopencv.com\u002Fcvui-gui-lib-built-on-top-of-opencv-drawing-primitives\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FUI-cvui) |\n|[Install Dlib on Windows](http:\u002F\u002Fwww.learnopencv.com\u002Finstall-dlib-on-windows\u002F) | |\n|[Install Dlib on Ubuntu](http:\u002F\u002Fwww.learnopencv.com\u002Finstall-dlib-on-ubuntu\u002F) | |\n|[Install OpenCV3 on Ubuntu](http:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv3-on-ubuntu\u002F) | |\n|[Read, Write and Display a video using OpenCV ( C++\u002F Python )](http:\u002F\u002Fwww.learnopencv.com\u002Fread-write-and-display-a-video-using-opencv-cpp-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FVideoReadWriteDisplay) |\n|[Install Dlib on MacOS](http:\u002F\u002Fwww.learnopencv.com\u002Finstall-dlib-on-macos\u002F) | |\n|[Install OpenCV 3 on MacOS](http:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv3-on-macos\u002F) | |\n|[Install OpenCV 3 on Windows](http:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv3-on-windows\u002F) | |\n|[Get OpenCV Build Information ( getBuildInformation )](http:\u002F\u002Fwww.learnopencv.com\u002Fget-opencv-build-information-getbuildinformation\u002F) | |\n|[Color spaces in OpenCV (C++ \u002F Python)](http:\u002F\u002Fwww.learnopencv.com\u002Fcolor-spaces-in-opencv-cpp-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FColorSpaces)|\n|[Neural Networks : A 30,000 Feet View for Beginners](http:\u002F\u002Fwww.learnopencv.com\u002Fneural-networks-a-30000-feet-view-for-beginners\u002F) | |\n|[Alpha Blending using OpenCV (C++ \u002F Python)](http:\u002F\u002Fwww.learnopencv.com\u002Falpha-blending-using-opencv-cpp-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FAlphaBlending) |\n|[User stories : How readers of this blog are applying their knowledge to build applications](http:\u002F\u002Fwww.learnopencv.com\u002Fuser-stories-how-readers-of-this-blog-are-applying-their-knowledge-to-build-applications\u002F) | |\n|[How to select a bounding box ( ROI ) in OpenCV (C++\u002FPython) ?](http:\u002F\u002Fwww.learnopencv.com\u002Fhow-to-select-a-bounding-box-roi-in-opencv-cpp-python\u002F) | |\n|[Automatic Red Eye Remover using OpenCV (C++ \u002F Python)](http:\u002F\u002Fwww.learnopencv.com\u002Fautomatic-red-eye-remover-using-opencv-cpp-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FRedEyeRemover) |\n|[Bias-Variance Tradeoff in Machine Learning](http:\u002F\u002Fwww.learnopencv.com\u002Fbias-variance-tradeoff-in-machine-learning\u002F) | |\n|[Embedded Computer Vision: Which device should you choose?](http:\u002F\u002Fwww.learnopencv.com\u002Fembedded-computer-vision-which-device-should-you-choose\u002F) | |\n|[Object Tracking using OpenCV (C++\u002FPython)](http:\u002F\u002Fwww.learnopencv.com\u002Fobject-tracking-using-opencv-cpp-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Ftracking) |\n|[Handwritten Digits Classification : An OpenCV ( C++ \u002F Python ) Tutorial](http:\u002F\u002Fwww.learnopencv.com\u002Fhandwritten-digits-classification-an-opencv-c-python-tutorial\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fdigits-classification) |\n|[Training a better Haar and LBP cascade based Eye Detector using OpenCV](http:\u002F\u002Fwww.learnopencv.com\u002Ftraining-better-haar-lbp-cascade-eye-detector-opencv\u002F) | |\n|[Deep Learning Book Gift Recipients](http:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-book-gift-recipients\u002F) | |\n|[Minified OpenCV Haar and LBP Cascades](http:\u002F\u002Fwww.learnopencv.com\u002Fminified-opencv-haar-and-lbp-cascades\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FninjaEyeDetector)|\n|[Deep Learning Book Gift](http:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-book-gift\u002F) | |\n|[Histogram of Oriented Gradients](http:\u002F\u002Fwww.learnopencv.com\u002Fhistogram-of-oriented-gradients\u002F) | |\n|[Image Recognition and Object Detection : Part 1](http:\u002F\u002Fwww.learnopencv.com\u002Fimage-recognition-and-object-detection-part1\u002F) | |\n|[Head Pose Estimation using OpenCV and Dlib](http:\u002F\u002Fwww.learnopencv.com\u002Fhead-pose-estimation-using-opencv-and-dlib\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHeadPose) |\n|[Live CV : A Computer Vision Coding Application](http:\u002F\u002Fwww.learnopencv.com\u002Flive-cv\u002F) | |\n|[Approximate Focal Length for Webcams and Cell Phone Cameras](http:\u002F\u002Fwww.learnopencv.com\u002Fapproximate-focal-length-for-webcams-and-cell-phone-cameras\u002F) | |\n|[Configuring Qt for OpenCV on OSX](http:\u002F\u002Fwww.learnopencv.com\u002Fconfiguring-qt-for-opencv-on-osx\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fqt-test) |\n|[Rotation Matrix To Euler Angles](http:\u002F\u002Fwww.learnopencv.com\u002Frotation-matrix-to-euler-angles\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FRotationMatrixToEulerAngles) |\n|[Speeding up Dlib’s Facial Landmark Detector](http:\u002F\u002Fwww.learnopencv.com\u002Fspeeding-up-dlib-facial-landmark-detector\u002F) | |\n|[Warp one triangle to another using OpenCV ( C++ \u002F Python )](http:\u002F\u002Fwww.learnopencv.com\u002Fwarp-one-triangle-to-another-using-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FWarpTriangle) |\n|[Average Face : OpenCV ( C++ \u002F Python ) Tutorial](http:\u002F\u002Fwww.learnopencv.com\u002Faverage-face-opencv-c-python-tutorial\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFaceAverage) |\n|[Face Swap using OpenCV ( C++ \u002F Python )](http:\u002F\u002Fwww.learnopencv.com\u002Fface-swap-using-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFaceSwap) |\n|[Face Morph Using OpenCV — C++ \u002F Python](http:\u002F\u002Fwww.learnopencv.com\u002Fface-morph-using-opencv-cpp-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFaceMorph) |\n|[Deep Learning Example using NVIDIA DIGITS 3 on EC2](http:\u002F\u002Fwww.learnopencv.com\u002Fdeep-learning-example-using-nvidia-digits-3-on-ec2\u002F) | |\n|[NVIDIA DIGITS 3 on EC2](http:\u002F\u002Fwww.learnopencv.com\u002Fnvidia-digits-3-on-ec2\u002F) | |\n|[Homography Examples using OpenCV ( Python \u002F C ++ )](http:\u002F\u002Fwww.learnopencv.com\u002Fhomography-examples-using-opencv-python-c\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHomography) |\n|[Filling holes in an image using OpenCV ( Python \u002F C++ )](http:\u002F\u002Fwww.learnopencv.com\u002Ffilling-holes-in-an-image-using-opencv-python-c\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FHoles) |\n|[How to find frame rate or frames per second (fps) in OpenCV ( Python \u002F C++ ) ?](http:\u002F\u002Fwww.learnopencv.com\u002Fhow-to-find-frame-rate-or-frames-per-second-fps-in-opencv-python-cpp\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFPS) |\n|[Delaunay Triangulation and Voronoi Diagram using OpenCV ( C++ \u002F Python)](http:\u002F\u002Fwww.learnopencv.com\u002Fdelaunay-triangulation-and-voronoi-diagram-using-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FDelaunay) |\n|[OpenCV (C++ vs Python) vs MATLAB for Computer Vision](http:\u002F\u002Fwww.learnopencv.com\u002Fopencv-c-vs-python-vs-matlab-for-computer-vision\u002F) | |\n|[Facial Landmark Detection](http:\u002F\u002Fwww.learnopencv.com\u002Ffacial-landmark-detection\u002F) | |\n|[Why does OpenCV use BGR color format ?](http:\u002F\u002Fwww.learnopencv.com\u002Fwhy-does-opencv-use-bgr-color-format\u002F) | |\n|[Computer Vision for Predicting Facial Attractiveness](http:\u002F\u002Fwww.learnopencv.com\u002Fcomputer-vision-for-predicting-facial-attractiveness\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FFacialAttractiveness) |\n|[applyColorMap for pseudocoloring in OpenCV ( C++ \u002F Python )](http:\u002F\u002Fwww.learnopencv.com\u002Fapplycolormap-for-pseudocoloring-in-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FColormap) |\n|[Image Alignment (ECC) in OpenCV ( C++ \u002F Python )](http:\u002F\u002Fwww.learnopencv.com\u002Fimage-alignment-ecc-in-opencv-c-python\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FImageAlignment) |\n|[How to find OpenCV version in Python and C++ ?](http:\u002F\u002Fwww.learnopencv.com\u002Fhow-to-find-opencv-version-python-cpp\u002F) | |\n|[Baidu banned from ILSVRC 2015](http:\u002F\u002Fwww.learnopencv.com\u002Fbaidu-banned-from-ilsvrc-2015\u002F) | |\n|[OpenCV Transparent API](http:\u002F\u002Fwww.learnopencv.com\u002Fopencv-transparent-api\u002F) | |\n|[How Computer Vision Solved the Greatest Soccer Mystery of All Time](http:\u002F\u002Fwww.learnopencv.com\u002Fhow-computer-vision-solved-the-greatest-soccer-mystery-of-all-times\u002F) | |\n|[Embedded Vision Summit 2015](http:\u002F\u002Fwww.learnopencv.com\u002Fembedded-vision-summit-2015\u002F) | |\n|[Read an Image in OpenCV ( Python, C++ )](http:\u002F\u002Fwww.learnopencv.com\u002Fread-an-image-in-opencv-python-cpp\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002Fimread) |\n|[Non-Photorealistic Rendering using OpenCV ( Python, C++ )](http:\u002F\u002Fwww.learnopencv.com\u002Fnon-photorealistic-rendering-using-opencv-python-c\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FNonPhotorealisticRendering) |\n|[Seamless Cloning using OpenCV ( Python , C++ )](http:\u002F\u002Fwww.learnopencv.com\u002Fseamless-cloning-using-opencv-python-cpp\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FSeamlessCloning) |\n|[OpenCV Threshold ( Python , C++ )](http:\u002F\u002Fwww.learnopencv.com\u002Fopencv-threshold-python-cpp\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FThreshold) |\n|[Blob Detection Using OpenCV ( Python, C++ )](http:\u002F\u002Fwww.learnopencv.com\u002Fblob-detection-using-opencv-python-c\u002F) | [Code](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Ftree\u002Fmaster\u002FBlobDetector) |\n|[Turn your OpenCV Code into a Web API in under 10 minutes — Part 1](http:\u002F\u002Fwww.learnopencv.com\u002Fturn-your-opencv-Code-into-a-web-api-in-under-10-minutes-part-1\u002F) | |\n|[How to compile OpenCV sample Code ?](http:\u002F\u002Fwww.learnopencv.com\u002Fhow-to-compile-opencv-sample-Code\u002F) | |\n|[Install OpenCV 3 on Yosemite ( OSX 10.10.x )](http:\u002F\u002Fwww.learnopencv.com\u002Finstall-opencv-3-on-yosemite-osx-10-10-x\u002F) | |","# LearnOpenCV 快速上手指南\n\nLearnOpenCV 是一个汇集了计算机视觉、深度学习和人工智能研究文章配套代码的开源仓库。本指南将帮助你快速配置环境并运行其中的示例项目。\n\n## 环境准备\n\n在开始之前，请确保你的开发环境满足以下要求：\n\n*   **操作系统**: Linux (推荐 Ubuntu 20.04+), macOS, 或 Windows (建议使用 WSL2)。\n*   **Python 版本**: Python 3.8 或更高版本 (推荐 3.10+)。\n*   **硬件加速**: 部分深度学习示例（如 YOLO, VLM, LLM 部署）强烈建议配备 NVIDIA GPU 并安装对应的 CUDA 驱动。\n*   **前置依赖**:\n    *   Git\n    *   pip (Python 包管理工具)\n    *   CMake (部分需要编译的 OpenCV 模块可能需要)\n\n> **国内开发者提示**：建议在安装 Python 依赖时使用国内镜像源（如清华源或阿里源）以加速下载。\n\n## 安装步骤\n\n### 1. 克隆仓库\n首先，将代码库克隆到本地：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv.git\ncd learnopencv\n```\n\n### 2. 创建虚拟环境\n为避免依赖冲突，建议为每个项目或整体创建一个独立的虚拟环境：\n\n```bash\npython -m venv venv\nsource venv\u002Fbin\u002Factivate  # Linux\u002FmacOS\n# 或在 Windows 上使用: venv\\Scripts\\activate\n```\n\n### 3. 安装基础依赖\n虽然不同子项目可能有特定的 `requirements.txt`，但大多数项目依赖基础的 OpenCV 和深度学习框架。你可以先安装通用基础包：\n\n```bash\n# 使用国内镜像源加速安装\npip install -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple opencv-python-headless torch torchvision torchaudio --extra-index-url https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Fcu118\n```\n\n> **注意**：具体的子项目（如 `YOLO26-instance-segmentation` 或 `LLaVA`）通常在其文件夹内包含独立的 `requirements.txt`。进入具体项目目录后，请优先执行该项目下的安装命令：\n> ```bash\n> cd \u003C具体项目文件夹名称>\n> pip install -r requirements.txt\n> ```\n\n## 基本使用\n\nLearnOpenCV 包含多个独立的项目演示。以下以通用的**物体检测**或**图像读取**为例，展示如何运行一个简单的 Python 脚本。\n\n### 示例：运行一个简单的 OpenCV 脚本\n\n假设你正在尝试运行仓库中某个基础示例（例如读取并显示图像，或运行一个简单的检测模型）：\n\n1.  **进入项目目录**：\n    选择一个包含代码的文件夹，例如 `FaceBlurPixelate` (人脸模糊) 或其他带有 `Code` 链接的项目。\n\n    ```bash\n    cd FaceBlurPixelate\n    ```\n\n2.  **查看脚本用法**：\n    大多数脚本支持 `-h` 或 `--help` 参数来查看使用说明。\n\n    ```bash\n    python main.py --help\n    ```\n\n3.  **运行示例**：\n    根据帮助信息传入必要的参数（通常是输入图片或视频路径）。以下是一个典型的运行命令结构：\n\n    ```bash\n    python main.py --input path\u002Fto\u002Fyour\u002Fimage.jpg --output result.jpg\n    ```\n\n    如果是基于深度学习模型的示例（如 YOLO 或 VLM），通常需要指定模型权重文件：\n\n    ```bash\n    python infer.py --weights yolov8n.pt --source 0\n    ```\n    *(注：`--source 0` 通常表示调用摄像头)*\n\n### 探索更多项目\n你可以浏览仓库根目录下的文件夹列表，每个文件夹对应一篇技术博客的实现代码。进入相应文件夹后，参照该文件夹内的 `README.md`（如果有）或直接运行主脚本即可体验最新的 AI 技术（如 RF-DETR, SAM-3, V-JEPA 2 等）。","某智慧零售团队正致力于开发一套实时顾客行为分析系统，需要在边缘设备上精准追踪多人动线并自动模糊人脸以符合隐私法规。\n\n### 没有 learnopencv 时\n- 开发者需从零复现复杂的 YOLO26 或 RF-DETR 算法，耗费数周调试实例分割与实时检测的代码兼容性。\n- 面对多目标追踪场景，缺乏成熟的 Roboflow 追踪器集成示例，导致人员身份频繁切换，数据准确率极低。\n- 为满足隐私合规，手动编写基于 YuNet 的人脸模糊逻辑效率低下，且难以在 Jetson 等边缘端实现低延迟推理。\n- 遇到模型部署瓶颈（如 NMS 后处理耗时）时，缺乏官方优化的无 NMS 推理方案，系统帧率无法达到实时要求。\n\n### 使用 learnopencv 后\n- 直接调用仓库中经过验证的 YOLO26 和 RF-DETR 演示代码，半天内即可跑通像素级实例分割功能，大幅缩短研发周期。\n- 复用现成的 Roboflow 追踪器集成脚本，轻松实现稳定流畅的多目标轨迹跟踪，无需担心算法底层实现细节。\n- 利用内置的 OpenCV YuNet 人脸模糊模块，快速部署实时的隐私保护功能，确保系统在采集瞬间即完成脱敏处理。\n- 借鉴 YOLO26 无 NMS 推理及 Jetson 端 LLM 部署的最佳实践，成功将系统延迟降低 40%，在边缘设备上实现丝滑运行。\n\nlearnopencv 通过提供生产级的代码范例与前沿算法落地指南，让开发者从重复造轮子中解放出来，专注于业务逻辑的创新与交付。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fspmallick_learnopencv_25bc0f80.png","spmallick","Satya Mallick","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fspmallick_cf6cc071.jpg","Computer Vision, Machine Learning, AI",null,"San Diego","http:\u002F\u002Fwww.learnopencv.com","https:\u002F\u002Fgithub.com\u002Fspmallick",[83,87,91,95,99,103,107,111,114,117],{"name":84,"color":85,"percentage":86},"Jupyter Notebook","#DA5B0B",98.1,{"name":88,"color":89,"percentage":90},"C","#555555",0.7,{"name":92,"color":93,"percentage":94},"Python","#3572A5",0.6,{"name":96,"color":97,"percentage":98},"Java","#b07219",0.3,{"name":100,"color":101,"percentage":102},"C++","#f34b7d",0.2,{"name":104,"color":105,"percentage":106},"Cuda","#3A4E3A",0.1,{"name":108,"color":109,"percentage":110},"Shell","#89e051",0,{"name":112,"color":113,"percentage":110},"CMake","#DA3434",{"name":115,"color":116,"percentage":110},"HTML","#e34c26",{"name":118,"color":119,"percentage":110},"Makefile","#427819",22880,11716,"2026-04-17T23:39:29","未说明","部分项目（如 LLM 服务、VLM、3D 重建）需要 NVIDIA GPU，具体显存需求视模型而定（通常建议 8GB+），CUDA 版本未明确指定；部分项目支持边缘设备（如 Jetson Nano\u002FOrin）或 Arduino。","未说明（大型模型训练或推理通常建议 16GB+）",{"notes":127,"python":123,"dependencies":128},"该仓库是多个独立教程和演示代码的集合，并非单一工具，因此不同子目录（如 YOLO26、vLLM 部署、SAM-3 等）的环境需求差异巨大。部分项目专为边缘设备（NVIDIA Jetson）或微控制器（Arduino）设计。运行特定项目前，请务必查阅对应子目录下的具体要求或关联博客文章。",[129,130,131,132,133,134,135,136],"opencv-python","torch","transformers","ultralytics","vllm","langgraph","accelerate","roboflow",[15,14,13],[139,140,141,142,143,144,145,146,129,147,148,149,150],"computer-vision","machine-learning","ai","deep-learning","deep-neural-networks","deeplearning","computervision","opencv","opencv-library","opencv3","opencv-cpp","opencv-tutorial","2026-03-27T02:49:30.150509","2026-04-18T14:13:17.264135",[154,159,164,169,174,179],{"id":155,"question_zh":156,"answer_zh":157,"source_url":158},40062,"运行 EAST 文本检测模型时出现 'Can't open \"true\"' 或 'Can't create layer...RealDiv' 错误怎么办？","这通常是因为 OpenCV 版本过低导致的兼容性问题。如果您使用的是 OpenCV 3.4.1，请升级到至少 3.4.3 版本（建议使用 3.4.6 或更高）。同时确保 TensorFlow 版本兼容（如 1.14.0），低版本的 OpenCV DNN 模块无法正确解析某些模型层。","https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fissues\u002F245",{"id":160,"question_zh":161,"answer_zh":162,"source_url":163},40063,"运行示例代码时提示找不到模型文件或配置文件（如 .pb, .xml 文件）如何解决？","大多数情况下是因为没有预先下载所需的模型权重文件或校准数据。请检查项目说明文档，手动下载对应的模型文件并放置在代码指定的目录下。此外，如果是相对路径报错（如 '..\u002Fdata\u002F...'），请尝试改为直接路径（如 'data\u002F...'）或确认当前工作目录（CWD）是否正确。","https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fissues\u002F113",{"id":165,"question_zh":166,"answer_zh":167,"source_url":168},40064,"运行立体相机深度感知代码时报错 'Can't open file: ...stereo_rectify_maps.xml' 或 '_map1.empty()' 怎么办？","这是一个文件路径问题。代码默认使用相对路径（如 '..\u002Fdata\u002F...'）查找校准文件，但实际运行时当前工作目录可能不同。解决方法是将路径修改为相对于脚本位置的正确路径（例如去掉 '..\u002F' 直接使用 'data\u002Fstereo_rectify_maps.xml'），或者确保在包含 data 文件夹的根目录下运行脚本。","https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fissues\u002F758",{"id":170,"question_zh":171,"answer_zh":172,"source_url":173},40065,"GroundingDINO 微调脚本报错 'module groundingdino.util.train does not exist' 如何解决？","这是因为原脚本依赖了 GroundingDINO 仓库中未公开的内部模块。维护者已更新教程，移除了对该私有模块的依赖，改用本地训练工具，并修复了硬编码的数据集路径问题。请拉取最新的代码更新（commit bde2118e 之后），并确保环境配置为 Python 3.12 且 transformers 版本小于 4.40。","https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fissues\u002F1021",{"id":175,"question_zh":176,"answer_zh":177,"source_url":178},40066,"YOLOv3 在 OpenCV 上的运行速度为何远低于宣传的数值（如只有 4 FPS）？","宣传的速度通常是在高性能硬件（如多核 i7 CPU 或高端 GPU）上测得的基准数据。实际速度高度依赖于您的硬件配置。例如，在 GTX 1050 Ti 等入门级显卡上，FPS 较低是正常现象。此外，需确认是否启用了 GPU 加速（CUDA\u002FcuDNN），否则仅靠 CPU 运行速度会显著下降。","https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fissues\u002F273",{"id":180,"question_zh":181,"answer_zh":182,"source_url":183},40067,"在 Windows 上编译 Darknet 进行 ALPR 训练时经常卡死或中断怎么办？","这可能是由于显存不足或散热问题导致的。建议尝试以下措施：1. 在 cfg 文件中增加 'subdivisions' 参数（如设为 32 或 64）以减少单次批处理的显存占用；2. 避免使用免费版的 Google Colab（GPU 时常断开），建议在本地拥有稳定电源和散热的机器上运行；3. 监控 GPU 温度和功耗，确保未触发保护机制。","https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv\u002Fissues\u002F658",[185,190,195,200],{"id":186,"version":187,"summary_zh":188,"released_at":189},323579,"RF_DETR_Segmentation","发布 RF_DETR_Segmentation 的资产。","2026-04-07T04:10:44",{"id":191,"version":192,"summary_zh":193,"released_at":194},323580,"YOLO26_Keypoint_Estimation","YOLO26 关键点检测教程的图像和视频资源。\n\nSHA-256（`YOLO26_Keypoint_Estimation.zip`）：`46645d3ba845063e6d16c461b359459b0f6c5d8e78f37634b7ddb071fe41c03c`","2026-04-16T20:14:54",{"id":196,"version":197,"summary_zh":198,"released_at":199},323581,"Colorization","用于色彩化教程的模型资产。\n\n此版本托管了 `colorization_release_v2.caffemodel`，因为伯克利大学的原始下载链接已无法访问。该模型的原始署名仍归 `richzhang\u002Fcolorization` 项目的 Richard Zhang、Phillip Isola 和 Alexei A. Efros 所有。\n\n支持文件 `pts_in_hull.npy` 和 `colorization_deploy_v2.prototxt` 直接从上游 `caffe` 分支引入并包含在本仓库中。\n\nSHA-256 (`colorization_release_v2.caffemodel`)：`f5af1e602646328c792e1094f9876fe9cd4c09ac46fa886e5708a1abc89137b1`","2026-03-18T07:19:42",{"id":201,"version":202,"summary_zh":203,"released_at":204},323582,"Roboflow_Trackers","Roboflow Trackerer 的演示。","2026-03-17T19:07:20"]