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Glenn Jocher 2020-02-21 17:16:34 -08:00
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@ -147,31 +147,31 @@ $ 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 <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>**36.3** |29.1<br>51.8<br>52.3<br>**55.5** 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>**36.4** |29.1<br>51.8<br>52.3<br>**55.7**
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>**39.8** |33.0<br>55.4<br>56.9<br>**59.6** 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>**40.0** |33.0<br>55.4<br>56.9<br>**60.0**
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>**41.3** |34.9<br>57.7<br>59.5<br>**61.3** 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>**41.5** |34.9<br>57.7<br>59.5<br>**61.4**
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>**41.7** |35.4<br>58.2<br>60.7<br>**61.5** 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>**41.9** |35.4<br>58.2<br>60.7<br>**61.6**
```bash ```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
Namespace(batch_size=32, 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='last54.pt') Namespace(batch_size=32, 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='yolov3-spp-ultralytics.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB) Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
Class Images Targets P R mAP@0.5 F1: 100% 157/157 [04:25<00:00, 1.04it/s] Class Images Targets P R mAP@0.5 F1: 100% 157/157 [04:25<00:00, 1.01s/it]
all 5e+03 3.51e+04 0.0467 0.886 0.607 0.0875 all 5e+03 3.51e+04 0.0453 0.885 0.609 0.0852
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.415 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.417
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.615 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.616
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.443 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.448
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.245 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.242
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.458 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.462
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.531 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.522
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.341 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.337
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.559 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.559
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.611 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.611
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.441 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.436
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.658 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.659
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.748 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.741
``` ```
# Reproduce Our Results # Reproduce Our Results