From faab52913c510c719df4ec80dd08ee482d3c3417 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Thu, 26 Mar 2020 16:35:46 -0700 Subject: [PATCH] mAP updates --- README.md | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index f00bccde..c527cd00 100755 --- a/README.md +++ b/README.md @@ -153,28 +153,28 @@ YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive. YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |608 |16.6
33.1
37.0
**44.0** |35.4
58.2
60.7
**62.6** ```bash -$ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt --img 608 --augment +$ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt --img 640 --augment Namespace(augment=True, batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='coco2014.data', device='', img_size=608, iou_thres=0.7, 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) Class Images Targets P R mAP@0.5 F1: 100%|█████████| 313/313 [03:00<00:00, 1.74it/s] - all 5e+03 3.51e+04 0.357 0.727 0.622 0.474 + all 5e+03 3.51e+04 0.35 0.737 0.624 0.47 - Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.454 - Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.631 - Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.498 - Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.265 - Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.498 - Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.605 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.357 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.619 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.827 - Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.770 - Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.859 - Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.896 + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.457 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.635 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.502 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.282 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.501 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.589 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.359 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.621 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.828 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.772 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.861 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.893 -Speed: 20.2/2.4/22.6 ms inference/NMS/total per 608x608 image at batch-size 16 +Speed: 21.6/2.6/24.1 ms inference/NMS/total per 640x640 image at batch-size 16 ``` # Reproduce Our Results