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Common Pitfalls to Avoid in Object Detection Datasets - Object Detection Challenges & Solutions

LearnOpenCV
59.6K subscribers
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17K views 2 years ago #objectdetection #AI #computervision
Learn about the best practices in creating high-quality datasets for Object Detection. “Data is the new Oil” — Unrefined and unpolished data will only result in a “GIGO” (Garbage In, Garbage Out) system! Many Deep Learning practitioners ignore the importance of data quality while building the model and keep iterating over model building instead of improving their data. Here we discuss ideas on how to analyze your dataset and common pitfalls while creating the dataset. We also talk about how checking your data gives you insights into the quality of your dataset as well as tips on how to improve the data and, eventually, the model performance. We take an example of a freely available public dataset to discuss the various issues that you may encounter while solving an Object Detection problem. ⭐️ Time Stamps ⭐️ 0:00-00:22: Motivation 00:22-1:15: The Dataset 1:15-3:03: Analyzing the Dataset 3:03-4:29: Tip: Visualize the Dataset 4:29-6:14: Unders …...more
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Common Pitfalls to Avoid in Object Detection Datasets - Object Detection Challenges & Solutions
423Likes
17,583Views
2022May 17
Learn about the best practices in creating high-quality datasets for Object Detection. “Data is the new Oil” — Unrefined and unpolished data will only result in a “GIGO” (Garbage In, Garbage Out) system! Many Deep Learning practitioners ignore the importance of data quality while building the model and keep iterating over model building instead of improving their data. Here we discuss ideas on how to analyze your dataset and common pitfalls while creating the dataset. We also talk about how checking your data gives you insights into the quality of your dataset as well as tips on how to improve the data and, eventually, the model performance. We take an example of a freely available public dataset to discuss the various issues that you may encounter while solving an Object Detection problem. ⭐️ Time Stamps ⭐️ 0:00-00:22: Motivation 00:22-1:15: The Dataset 1:15-3:03: Analyzing the Dataset 3:03-4:29: Tip: Visualize the Dataset 4:29-6:14: Understanding the classes 6:14-7:54: Pitfall: Oversampling frames from a video 7:54-11:36: Data Variance vs Data Size 11:36-11:57: Tip: Compare Training and Validation Set 11:57-14:35: Training Validation Overlap 14:35-16:01: Tip: Check Data Statistics 16:01-17:01: Pitfall: Class Imbalance 17:01-20:33: Visualize Data Annotations 20:33-21:34: Pitfall: Miscalssified or Incorrect Labels 21:34-27:03: Pitfall: Missing / Wrong Labels 27:03-29:22 : Pitfall: inconsistent labels 29:22-31:11 : Summary 🖥️ On our blog - https://learnopencv.com we also share tutorials and code on topics like Image Processing, Image Classification, Object Detection, Face Detection, Face Recognition, YOLO, Segmentation, Pose Estimation, and many more using OpenCV(Python/C++), PyTorch, and TensorFlow. 🤖 Learn from the experts on AI: Computer Vision and AI Courses YOU have an opportunity to join the over 5300+ (and counting) researchers, engineers, and students that have benefited from these courses and take your knowledge of computer vision, AI, and deep learning to the next level.🤖 https://opencv.org/courses #️⃣ Social Media #️⃣ 📝 Linkedin:   / satyamall.  . 📱 Twitter:   / learnopencv   🔊 Facebook: https://www.facebook.com/profile.php?... 📸 Instagram:   / learnopencv   🔗 Reddit:   / spmallick   🔖Hashtags🔖 #AI #machinelearning #objectdetection #deeplearning #computervision #datasets #pitfalls #objecttracking #dataset #bestpractice

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LearnOpenCV

59.6K subscribers