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Glenn Jocher 2019-04-03 14:25:39 +02:00
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@ -115,13 +115,13 @@ Run `detect.py` with `webcam=True` to show a live webcam feed.
- Compare to darknet published results https://arxiv.org/abs/1804.02767. - Compare to darknet published results https://arxiv.org/abs/1804.02767.
<!--- <!---
%<i></i> | ultralytics/yolov3 fastest 5:52@416 (`pycocotools`) | darknet %<i></i> | ultralytics/yolov3 OR-NMS 5:52@416 (`pycocotools`) | darknet
--- | --- | --- --- | --- | ---
YOLOv3-320 | 51.9 (51.4) | 51.5 YOLOv3-320 | 51.9 (51.4) | 51.5
YOLOv3-416 | 55.0 (54.9) | 55.3 YOLOv3-416 | 55.0 (54.9) | 55.3
YOLOv3-608 | 57.5 (57.8) | 57.9 YOLOv3-608 | 57.5 (57.8) | 57.9
<i></i> | ultralytics/yolov3 MERGE 7:15@416 (`pycocotools`) | darknet <i></i> | ultralytics/yolov3 MERGE-NMS 7:15@416 (`pycocotools`) | darknet
--- | --- | --- --- | --- | ---
YOLOv3-320 | 52.3 (51.7) | 51.5 YOLOv3-320 | 52.3 (51.7) | 51.5
YOLOv3-416 | 55.4 (55.3) | 55.3 YOLOv3-416 | 55.4 (55.3) | 55.3
@ -133,11 +133,14 @@ YOLOv3-320 | 52.3 (51.8) | 51.5
YOLOv3-416 | 55.5 (55.4) | 55.3 YOLOv3-416 | 55.5 (55.4) | 55.3
YOLOv3-608 | 57.9 (58.2) | 57.9 YOLOv3-608 | 57.9 (58.2) | 57.9
---> --->
<i></i> | [ultralytics/yolov3](https://github.com/ultralytics/yolov3) with `pycocotools` | [darknet/yolov3](https://arxiv.org/abs/1804.02767) <i></i> | [ultralytics/yolov3](https://github.com/ultralytics/yolov3) | [darknet](https://arxiv.org/abs/1804.02767)
--- | --- | --- --- | --- | ---
YOLOv3-320 | 51.8 | 51.5 `YOLOv3 320` | 51.8 | 51.5
YOLOv3-416 | 55.4 | 55.3 `YOLOv3 416` | 55.4 | 55.3
YOLOv3-608 | 58.2 | 57.9 `YOLOv3 608` | 58.2 | 57.9
`YOLOv3-spp 320` | 52.4 | -
`YOLOv3-spp 416` | 56.5 | -
`YOLOv3-spp 608` | 60.7 | 60.6
``` bash ``` bash
sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3 sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
@ -145,25 +148,6 @@ sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3 sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
cd yolov3 cd yolov3
python3 test.py --save-json --conf-thres 0.001 --img-size 416
Namespace(batch_size=32, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3.weights')
Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)
Image Total P R mAP
Calculating mAP: 100%|█████████████████████████████████| 157/157 [08:34<00:00, 2.53s/it]
5000 5000 0.0896 0.756 0.555
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.312
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.554
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.317
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.145
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.343
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.452
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.268
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.411
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.435
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.244
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.477
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.587
python3 test.py --save-json --conf-thres 0.001 --img-size 608 --batch-size 16 python3 test.py --save-json --conf-thres 0.001 --img-size 608 --batch-size 16
Namespace(batch_size=16, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3.weights') Namespace(batch_size=16, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3.weights')
Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80) Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)
@ -182,8 +166,31 @@ Calculating mAP: 100%|███████████████████
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.309
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494 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 ] = 0.577 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577
python3 test.py --weights weights/yolov3-spp.weights --cfg cfg/yolov3-spp.cfg --save-json --img-size 608 --batch-size 8
Namespace(batch_size=8, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)
Image Total P R mAP
Calculating mAP: 100%|█████████████████████████████████| 625/625 [07:01<00:00, 1.56it/s]
5000 5000 0.12 0.81 0.611
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.366
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.607
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.386
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.207
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.391
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.485
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.296
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.464
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.494
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.331
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.517
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.618
``` ```
# Citation
[![DOI](https://zenodo.org/badge/146165888.svg)](https://zenodo.org/badge/latestdoi/146165888)
# Contact # Contact
For questions or comments please contact Glenn Jocher at glenn.jocher@ultralytics.com or visit us at https://contact.ultralytics.com. Issues should be raised directly in the repository. For additional questions or comments please email Glenn Jocher at glenn.jocher@ultralytics.com or visit us at https://contact.ultralytics.com/contact.