Training a YOLOv2 model with the Darknet object detection framework for real-time detection of playing cards
I ran YOLOv3 on my own generated dataset of 60,000 images. Results:
mAP | IoU | F1-Score |
---|---|---|
99.86% | 82.92% | 0.99 |
- Only tested on Bicycle cards. I chose this deck due to its popularity in commercial use.
- Update to use a PyTorch version of YOLO
- Upload dataset to Kaggle