From 0fe40cb6873637a78e4fd6b06331d3686844ba5c Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Tue, 26 Nov 2019 12:34:47 -1000 Subject: [PATCH] updates --- README.md | 65 ++++++++++++++++++++----------------------------------- 1 file changed, 24 insertions(+), 41 deletions(-) diff --git a/README.md b/README.md index cc8ce866..87ac3554 100755 --- a/README.md +++ b/README.md @@ -138,54 +138,37 @@ Success: converted 'weights/yolov3-spp.pt' to 'converted.weights' # mAP - `test.py --weights weights/yolov3.weights` tests official YOLOv3 weights. -- `test.py --weights weights/last.pt` tests most recent checkpoint. -- `test.py --weights weights/best.pt` tests best checkpoint. +- `test.py --weights weights/last.pt` tests latest checkpoint. - Compare to darknet published results https://arxiv.org/abs/1804.02767. -[ultralytics/yolov3](https://github.com/ultralytics/yolov3) mAP@0.5 ([darknet](https://arxiv.org/abs/1804.02767)-reported mAP@0.5) +[ultralytics/yolov3](https://github.com/ultralytics/yolov3) mAP@0.5 vs. [darknet](https://arxiv.org/abs/1804.02767)-reported mAP@0.5 - | 320 | 416 | 608 ---- | --- | --- | --- -`YOLOv3` | 51.8 (51.5) | 55.4 (55.3) | 58.2 (57.9) -`YOLOv3-SPP` | 53.7 | 57.7 | 60.7 (60.6) -`YOLOv3-tiny` | 29.0 | 32.9 (33.1) | 35.5 + | 320 | 416 | 608 +--- | --- | --- | --- +darknet `YOLOv3-tiny` | 29.0 | 33.1 | 35.5 +darknet `YOLOv3` | 51.5 | 55.3 | 57.9 +darknet `YOLOv3-SPP` | 52.3 | 56.8 | **60.6** +ultralytics `YOLOv3-SPP` | **53.9** | **58.7** | 60.1 ```bash -$ python3 test.py --save-json --img-size 608 -Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights') +$ python3 test.py --save-json --img-size 608 --weights ultralytics68.pt +Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', device='', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='ultralytics68.pt') Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB) - Class Images Targets P R mAP F1: 100% 313/313 [07:40<00:00, 2.34s/it] - all 5e+03 3.58e+04 0.119 0.788 0.594 0.201 - Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.367 <--- - Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.607 <--- - Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.387 - Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.208 - Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.392 - Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.297 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.465 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.495 - Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.332 - Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.518 - Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.621 -$ python3 test.py --save-json --img-size 416 -Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3s-ultralytics.pt') -Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB) - Class Images Targets P R mAP F1: 100% 313/313 [07:01<00:00, 1.41s/it] - all 5e+03 3.58e+04 0.11 0.739 0.569 0.185 - Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.373 - Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.577 - Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.392 - Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.175 - Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.403 - Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.537 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.313 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.482 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.501 - Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.266 - Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.541 - Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.693 + Class Images Targets P R mAP@0.5 F1: 100% 313/313 [06:52<00:00, 1.24it/s] + all 5e+03 3.58e+04 0.107 0.779 0.59 0.182 + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.398 <--- + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.601 <--- + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.425 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.438 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.505 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.325 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.519 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.543 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.366 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.584 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.665 ``` # Citation