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mAP updates

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Glenn Jocher 2020-03-26 16:22:58 -07:00
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@ -153,28 +153,28 @@ YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.
YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |608 |16.6<br>33.1<br>37.0<br>**44.0** |35.4<br>58.2<br>60.7<br>**62.6**
```bash
$ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt --img 608
$ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt --img 608 --augment
Namespace(batch_size=16, cfg='yolov3-spp.cfg', conf_thres=0.001, data='data/coco2014.data', device='', img_size=608, iou_thres=0.6, save_json=True, single_cls=False, task='test', weights='weights/yolov3-spp-ultralytics.pt')
Namespace(augment=True, batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='coco2014.data', device='', img_size=608, iou_thres=0.7, save_json=True, single_cls=False, task='test', weights='weights/yolov3-spp-ultralytics.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
Class Images Targets P R mAP@0.5 F1: 100%|█████| 157/157 [02:46<00:00, 1.06s/it]
all 5e+03 3.51e+04 0.515 0.665 0.61 0.577
Class Images Targets P R mAP@0.5 F1: 100%|█████████| 313/313 [03:00<00:00, 1.74it/s]
all 5e+03 3.51e+04 0.357 0.727 0.622 0.474
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.434
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.626
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.469
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.263
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.480
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.547
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.346
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.617
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.786
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.730
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.836
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.863
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.454
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.631
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.498
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.265
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.498
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.605
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.357
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.619
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.827
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.770
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.859
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.896
Speed: 6.9/2.1/9.0 ms inference/NMS/total per 608x608 image at batch-size 16
Speed: 20.2/2.4/22.6 ms inference/NMS/total per 608x608 image at batch-size 16
```
# Reproduce Our Results