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Glenn Jocher 2019-12-27 11:30:27 -08:00
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@ -152,24 +152,24 @@ YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**YOLOv3-SPP ultralytics** |512 |16.6<br>
YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**YOLOv3-SPP ultralytics** |608 |16.6<br>33.1<br>37.0<br>**41.1** |35.4<br>58.2<br>60.7<br>**61.5** YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**YOLOv3-SPP ultralytics** |608 |16.6<br>33.1<br>37.0<br>**41.1** |35.4<br>58.2<br>60.7<br>**61.5**
```bash ```bash
$ python3 test.py --img-size 608 --iou-thr 0.5 --weights ultralytics68.pt --cfg yolov3-spp.cfg $ python3 test.py --img-size 608 --iou-thr 0.6 --weights ultralytics68.pt --cfg yolov3-spp.cfg
Namespace(batch_size=32, cfg='yolov3-spp.cfg', conf_thres=0.001, data='data/coco2014.data', device='', img_size=608, iou_thres=0.5, save_json=True, task='test', weights='ultralytics68.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, task='test', weights='ultralytics68.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB) Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
Class Images Targets P R mAP@0.5 F1: 100% 157/157 [04:13<00:00, 1.16it/s] Class Images Targets P R mAP@0.5 F1: 100% 157/157 [03:30<00:00, 1.16it/s]
all 5e+03 3.51e+04 0.0437 0.88 0.607 0.0822 all 5e+03 3.51e+04 0.0353 0.891 0.606 0.0673
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.406 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.409
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.615 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.615
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.431 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.437
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.238 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 ] = 0.444 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.448
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.516 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.519
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= 1 ] = 0.337
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.548 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 ] = 0.592 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.612
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.418 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.438
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.635 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.658
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.729 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.746
``` ```
# Reproduce Our Results # Reproduce Our Results