inference speed and mAP updates
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README.md
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README.md
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@ -138,24 +138,24 @@ Namespace(augment=True, batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.00
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Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
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Class Images Targets P R mAP@0.5 F1: 100%|█████████| 313/313 [03:00<00:00, 1.74it/s]
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all 5e+03 3.51e+04 0.375 0.743 0.639 0.493
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all 5e+03 3.51e+04 0.375 0.743 0.64 0.492
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.455
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.646
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.496
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.263
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.500
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.501
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.596
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.362
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.361
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.597
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.666
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.491
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.492
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.719
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.808
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.810
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Speed: 21.3/3.0/24.4 ms inference/NMS/total per 640x640 image at batch-size 16
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Speed: 17.5/2.5/20.1 ms inference/NMS/total per 640x640 image at batch-size 16
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```
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<!-- Speed: 12.2/2.3/14.5 ms inference/NMS/total per 608x608 image at batch-size 1
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<!-- Speed: 11.5/2.1/13.6 ms inference/NMS/total per 608x608 image at batch-size 1
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# Reproduce Our Results
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