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mAP increases

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Glenn Jocher 2020-03-26 14:14:52 -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 <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>**36.6** |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)** |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>**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)** |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>**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)** |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>**42.1** |35.4<br>58.2<br>60.7<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>**44.0** |35.4<br>58.2<br>60.7<br>**62.6**
```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='weights/yolov3-spp-ultralytics.pt') 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')
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB) 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] 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 0.51 0.667 0.611 0.574 all 5e+03 3.51e+04 0.515 0.665 0.61 0.577
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.419 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.434
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.618 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.626
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.448 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.469
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.247 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 ] = 0.462 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 ] = 0.534 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 ] = 0.341 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 ] = 0.557 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 ] = 0.606 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 ] = 0.440 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 ] = 0.649 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 ] = 0.735 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.863
Speed: 6.5/1.5/8.1 ms inference/NMS/total per 608x608 image at batch-size 32 Speed: 6.9/2.1/9.0 ms inference/NMS/total per 608x608 image at batch-size 16
``` ```
# Reproduce Our Results # Reproduce Our Results