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

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.

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