HulkMaker
commented
5 years ago python3 test.py --conf-thres 0.001 --save-json Namespace(batch_size=32, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights') Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB) Class Images Targets P R mAP F1 Computing mAP: 100%|███████████████████████████████| 157/157 [06:02<00:00, 1.82s/it] all 5e+03 3.58e+04 0.109 0.773 0.57 0.186 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.335 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.565 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.349 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.360 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.493 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.280 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.432 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.458 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.255 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.620
Great work! But do you have any performance result on COCO/PASCAL?