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