PASCAL VOC2007 Test
| Model | Original | Ours (weight conversion) |
|---|---|---|
| YOLOv2 | 75.8 % * | 75.8 % |
| YOLOv2 tiny | 54.0 % ** | 53.5 % |
| YOLOv3 | 80.2 % | 80.2 % |
Scores are mean Average Precision (mAP) with PASCAL VOC2007 metric.
*: Although the original paper [1] reports 76.8 %, the darknet implementation and the provided weights achieved the lower score.
Similar issue is reported here.
**: Although the author's website reports 57.1 %, the darknet implementation and the provided weights achieved the lower score.
Detect objects in an given image. This demo downloads Pascal VOC pretrained model automatically if a pretrained model path is not given.
$ python demo.py [--model yolo_v2|yolo_v2_tiny|yolo_v3] [--gpu <gpu>] [--pretrained-model <model_path>] <image>.jpg
Convert *.weights to *.npz. YOLOv2, YOLOv2 tiny, and YOLOv3 are supported.
Note that the number of classes should be specified if it is not 80 (the number of classes in COCO).
$ python darknet2npz.py [--model yolo_v2|yolo_v2_tiny|yolo_v3] [--n-fg-class <#fg_class>] <source>.weights <target>.npz
The evaluation can be conducted using chainercv/examples/detection/eval_detection.py.
- Joseph Redmon et al. "YOLO9000: Better, Faster, Stronger" CVPR 2017.
- Joseph Redmon et al. "YOLOv3: An Incremental Improvement" arXiv 2018.