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Glenn Jocher 2019-11-26 12:34:47 -10:00
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@ -138,54 +138,37 @@ Success: converted 'weights/yolov3-spp.pt' to 'converted.weights'
# mAP # mAP
- `test.py --weights weights/yolov3.weights` tests official YOLOv3 weights. - `test.py --weights weights/yolov3.weights` tests official YOLOv3 weights.
- `test.py --weights weights/last.pt` tests most recent checkpoint. - `test.py --weights weights/last.pt` tests latest checkpoint.
- `test.py --weights weights/best.pt` tests best checkpoint.
- Compare to darknet published results https://arxiv.org/abs/1804.02767. - Compare to darknet published results https://arxiv.org/abs/1804.02767.
[ultralytics/yolov3](https://github.com/ultralytics/yolov3) mAP@0.5 ([darknet](https://arxiv.org/abs/1804.02767)-reported mAP@0.5) [ultralytics/yolov3](https://github.com/ultralytics/yolov3) mAP@0.5 vs. [darknet](https://arxiv.org/abs/1804.02767)-reported mAP@0.5
<i></i> | 320 | 416 | 608 <i></i> | 320 | 416 | 608
--- | --- | --- | --- --- | --- | --- | ---
`YOLOv3` | 51.8 (51.5) | 55.4 (55.3) | 58.2 (57.9) darknet `YOLOv3-tiny` | 29.0 | 33.1 | 35.5
`YOLOv3-SPP` | 53.7 | 57.7 | 60.7 (60.6) darknet `YOLOv3` | 51.5 | 55.3 | 57.9
`YOLOv3-tiny` | 29.0 | 32.9 (33.1) | 35.5 darknet `YOLOv3-SPP` | 52.3 | 56.8 | **60.6**
ultralytics `YOLOv3-SPP` | **53.9** | **58.7** | 60.1
```bash ```bash
$ python3 test.py --save-json --img-size 608 $ python3 test.py --save-json --img-size 608 --weights ultralytics68.pt
Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights') Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', device='', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='ultralytics68.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB) Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
Class Images Targets P R mAP F1: 100% 313/313 [07:40<00:00, 2.34s/it]
all 5e+03 3.58e+04 0.119 0.788 0.594 0.201
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.367 <---
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.607 <---
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.387
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.208
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.392
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.297
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.465
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.495
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.332
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.518
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.621
$ python3 test.py --save-json --img-size 416 Class Images Targets P R mAP@0.5 F1: 100% 313/313 [06:52<00:00, 1.24it/s]
Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3s-ultralytics.pt') all 5e+03 3.58e+04 0.107 0.779 0.59 0.182
Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB) Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.398 <---
Class Images Targets P R mAP F1: 100% 313/313 [07:01<00:00, 1.41s/it] Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.601 <---
all 5e+03 3.58e+04 0.11 0.739 0.569 0.185 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.425
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.373 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.577 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.438
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.392 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.505
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.175 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.325
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.403 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.519
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.537 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.543
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.313 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.366
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.482 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.584
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.501 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.665
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.266
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.541
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.693
``` ```
# Citation # Citation