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mAP updates

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Glenn Jocher 2020-03-27 13:52:07 -07:00
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@ -147,34 +147,34 @@ $ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt
<i></i> |Size |COCO mAP<br>@0.5...0.95 |COCO mAP<br>@0.5
--- | --- | --- | ---
YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |320 |14.0<br>28.7<br>30.5<br>**38.9** |29.1<br>51.8<br>52.3<br>**56.9**
YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |416 |16.0<br>31.2<br>33.9<br>**42.5** |33.0<br>55.4<br>56.9<br>**61.1**
YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |512 |16.6<br>32.7<br>35.6<br>**43.6** |34.9<br>57.7<br>59.5<br>**62.5**
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**
YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |320 |14.0<br>28.7<br>30.5<br>**37.0** |29.1<br>51.8<br>52.3<br>**56.0**
YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |416 |16.0<br>31.2<br>33.9<br>**40.7** |33.0<br>55.4<br>56.9<br>**60.4**
YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |512 |16.6<br>32.7<br>35.6<br>**42.0** |34.9<br>57.7<br>59.5<br>**61.9**
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>**42.4** |35.4<br>58.2<br>60.7<br>**62.0**
```bash
$ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt --img 640 --augment
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')
Namespace(augment=True, batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='coco2014.data', device='', img_size=640, iou_thres=0.6, save_json=True, single_cls=False, task='test', weights='weight
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
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 0.35 0.737 0.624 0.47
all 5e+03 3.51e+04 0.396 0.731 0.634 0.509
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.457
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.635
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.502
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.282
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.501
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.589
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.359
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.621
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.828
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.772
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.861
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.893
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.447
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.641
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.485
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.271
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.492
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.583
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 ] = 0.587
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.652
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.488
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.701
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.787
Speed: 21.6/2.6/24.1 ms inference/NMS/total per 640x640 image at batch-size 16
Speed: 21.3/3.0/24.4 ms inference/NMS/total per 640x640 image at batch-size 16
```
# Reproduce Our Results