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@ -84,11 +84,48 @@ Run `detect.py` with `webcam=True` to show a live webcam feed.
**PyTorch** format:
- https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI
# Validation mAP
# mAP
Run `test.py` to validate the official YOLOv3 weights `weights/yolov3.weights` against the 5000 validation images. You should obtain a .584 mAP at `--img-size 416`, or .586 at `--img-size 608` using this repo, compared to .579 at 608 x 608 reported in darknet (https://arxiv.org/abs/1804.02767).
Run `test.py --save-json --conf-thres 0.005` to test the official YOLOv3 weights `weights/yolov3.weights` against the 5000 validation images. Compare to .579 at 608 x 608 reported in darknet (https://arxiv.org/abs/1804.02767).
Run `test.py --weights weights/latest.pt` to validate against the latest training results. **Default training settings produce a 0.522 mAP at epoch 62.** Hyperparameter settings and loss equation changes affect these results significantly, and additional trade studies may be needed to further improve this.
Run `test.py --weights weights/latest.pt` to validate against the latest training results. Hyperparameter settings and loss equation changes affect these results significantly, and additional trade studies may be needed to further improve this.
``` bash
sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
# bash yolov3/data/get_coco_dataset.sh
sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
cd yolov3 && python3 test.py --save-json --conf-thres 0.005
...
Namespace(batch_size=32, cfg='cfg/yolov3.cfg', conf_thres=0.005, data_cfg='cfg/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.45, save_json=True, weights='weights/yolov3.weights')
loading annotations into memory...
Done (t=4.17s)
creating index...
index created!
Loading and preparing results...
DONE (t=1.75s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=39.30s).
Accumulating evaluation results...
DONE (t=4.63s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.307
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.545
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.309
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.140
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.333
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.453
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.266
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.396
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.415
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.222
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.449
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.575
```
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