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Glenn Jocher 2020-02-29 01:15:23 -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
--- | --- | --- | ---
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>**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>**41.5** |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>**41.9** |35.4<br>58.2<br>60.7<br>**61.6**
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>**36.6** |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>**40.4** |33.0<br>55.4<br>56.9<br>**60.2**
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>**41.6** |34.9<br>57.7<br>59.5<br>**61.7**
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>**42.1** |35.4<br>58.2<br>60.7<br>**61.7**
```bash
$ 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='yolov3-spp-ultralytics.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
Namespace(batch_size=32, cfg='yolov3-spp', 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='last82.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
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 0.0453 0.885 0.609 0.0852
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.417
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.616
Class Images Targets P R mAP@0.5 F1: 100% 157/157 [03:12<00:00, 1.50it/s]
all 5e+03 3.51e+04 0.0573 0.871 0.611 0.106
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.419
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.618
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.448
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.242
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.247
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 ] = 0.522
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=100 ] = 0.611
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 ] = 0.659
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.741
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.534
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= 10 ] = 0.557
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.606
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.440
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.649
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.735
```
# Reproduce Our Results