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@ -92,54 +92,56 @@ Run `detect.py` with `webcam=True` to show a live webcam feed.
# Pretrained Weights # Pretrained Weights
**Darknet** format: - Darknet `*.weights` format: https://pjreddie.com/media/files/yolov3.weights
- https://pjreddie.com/media/files/yolov3.weights - PyTorch `*.pt` format: https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI
- https://pjreddie.com/media/files/yolov3-tiny.weights
**PyTorch** format:
- https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI
# mAP # mAP
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). - Use `test.py --weights weights/yolov3.weights` to test the official YOLOv3 weights.
- Use `test.py --weights weights/latest.pt` to test the latest training results.
- Compare to official darknet results from https://arxiv.org/abs/1804.02767.
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. <i></i> | ultralytics/yolov3 | darknet
--- | ---| ---
YOLOv3-320 | 51.3 | 51.5
YOLOv3-416 | 54.9 | 55.3
YOLOv3-608 | 57.9 | 57.9
``` bash ``` bash
sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3 sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
# bash yolov3/data/get_coco_dataset.sh # 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 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 cd yolov3
... python3 test.py --save-json --conf-thres 0.001 --img-size 416
Namespace(batch_size=32, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.45, save_json=True, weights='weights/yolov3.weights')
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.308
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.549
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.310
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.334
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.454
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.267
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.403
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.428
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.585
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') python3 test.py --save-json --conf-thres 0.001 --img-size 608 --batch-size 16
Namespace(batch_size=16, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.45, save_json=True, weights='weights/yolov3.weights')
loading annotations into memory... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328
Done (t=4.17s) Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.579
creating index... Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.335
index created! Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190
Loading and preparing results... Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.357
DONE (t=1.75s) Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428
creating index... Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.279
index created! Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.429
Running per image evaluation... Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456
Evaluate annotation type *bbox* Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.299
DONE (t=39.30s). Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.483
Accumulating evaluation results... Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.572
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
``` ```
# Contact # Contact