add yolov3-spp-ultralytics.pt
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Glenn Jocher
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README.md
42
README.md
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@ -139,39 +139,39 @@ Success: converted 'weights/yolov3-spp.pt' to 'converted.weights'
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# mAP
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```bash
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python3 test.py --weights ... --cfg ...
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$ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt
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```
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- mAP@0.5 run at `--iou-thr 0.5`, mAP@0.5...0.95 run at `--iou-thr 0.7`
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- YOLOv3-SPP ultralytics is `ultralytics68.pt` with `yolov3-spp.cfg`
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- Darknet results: https://arxiv.org/abs/1804.02767
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<i></i> |Size |COCO mAP<br>@0.5...0.95 |COCO mAP<br>@0.5
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--- | --- | --- | ---
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YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**YOLOv3-SPP ultralytics** |320 |14.0<br>28.7<br>30.5<br>**35.6** |29.1<br>51.8<br>52.3<br>**55.4**
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YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**YOLOv3-SPP ultralytics** |416 |16.0<br>31.2<br>33.9<br>**39.1** |33.0<br>55.4<br>56.9<br>**59.6**
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YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**YOLOv3-SPP ultralytics** |512 |16.6<br>32.7<br>35.6<br>**40.6** |34.9<br>57.7<br>59.5<br>**61.4**
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YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**YOLOv3-SPP ultralytics** |608 |16.6<br>33.1<br>37.0<br>**41.1** |35.4<br>58.2<br>60.7<br>**61.7**
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YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |320 |14.0<br>28.7<br>30.5<br>**36.3** |29.1<br>51.8<br>52.3<br>**55.5**
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YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |416 |16.0<br>31.2<br>33.9<br>**39.8** |33.0<br>55.4<br>56.9<br>**59.6**
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YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |512 |16.6<br>32.7<br>35.6<br>**41.3** |34.9<br>57.7<br>59.5<br>**61.3**
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YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |608 |16.6<br>33.1<br>37.0<br>**41.7** |35.4<br>58.2<br>60.7<br>**61.5**
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```bash
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$ python3 test.py --img-size 608 --iou-thr 0.6 --weights ultralytics68.pt --cfg yolov3-spp.cfg
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$ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt --img 608
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Namespace(batch_size=32, cfg='yolov3-spp.cfg', conf_thres=0.001, data='data/coco2014.data', device='', img_size=608, iou_thres=0.6, save_json=True, task='test', weights='ultralytics68.pt')
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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% 157/157 [03:30<00:00, 1.16it/s]
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all 5e+03 3.51e+04 0.0353 0.891 0.606 0.0673
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.409
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Namespace(batch_size=32, cfg='yolov3-spp.cfg', conf_thres=0.001, data='data/coco2014.data', device='', img_size=608, iou_thres=0.6, save_json=True, single_cls=False, task='test', weights='last54.pt')
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Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
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Class Images Targets P R mAP@0.5 F1: 100% 157/157 [04:25<00:00, 1.04it/s]
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all 5e+03 3.51e+04 0.0467 0.886 0.607 0.0875
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.415
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.615
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.437
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.242
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.448
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.519
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.337
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.557
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.612
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.438
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.443
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.245
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.458
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.531
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.341
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.559
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.611
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.441
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.658
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.746
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.748
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```
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# Reproduce Our Results
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@ -159,7 +159,7 @@ if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path')
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parser.add_argument('--names', type=str, default='data/coco.names', help='*.names path')
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parser.add_argument('--weights', type=str, default='weights/ultralytics68.pt', help='path to weights file')
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parser.add_argument('--weights', type=str, default='weights/yolov3-spp-ultralytics.pt', help='weights path')
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parser.add_argument('--source', type=str, default='data/samples', help='source') # input file/folder, 0 for webcam
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parser.add_argument('--output', type=str, default='output', help='output folder') # output folder
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parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
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@ -434,7 +434,8 @@ def attempt_download(weights):
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'darknet53.conv.74': '1WUVBid-XuoUBmvzBVUCBl_ELrzqwA8dJ',
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'yolov3-tiny.conv.15': '1Bw0kCpplxUqyRYAJr9RY9SGnOJbo9nEj',
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'ultralytics49.pt': '158g62Vs14E3aj7oPVPuEnNZMKFNgGyNq',
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'ultralytics68.pt': '1Jm8kqnMdMGUUxGo8zMFZMJ0eaPwLkxSG'}
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'ultralytics68.pt': '1Jm8kqnMdMGUUxGo8zMFZMJ0eaPwLkxSG',
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'yolov3-spp-ultralytics.pt': '1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4'}
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file = Path(weights).name
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if file in d:
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2
test.py
2
test.py
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@ -210,7 +210,7 @@ if __name__ == '__main__':
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parser = argparse.ArgumentParser(prog='test.py')
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parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path')
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parser.add_argument('--data', type=str, default='data/coco2014.data', help='*.data path')
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parser.add_argument('--weights', type=str, default='weights/yolov3-spp.weights', help='path to weights file')
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parser.add_argument('--weights', type=str, default='weights/yolov3-spp-ultralytics.pt', help='weights path')
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parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
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parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
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parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
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2
train.py
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train.py
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@ -406,7 +406,7 @@ if __name__ == '__main__':
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parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
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parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
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parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
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parser.add_argument('--weights', type=str, default='weights/ultralytics68.pt', help='initial weights')
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parser.add_argument('--weights', type=str, default='weights/yolov3-spp-ultralytics.pt', help='initial weights path')
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parser.add_argument('--arc', type=str, default='default', help='yolo architecture') # default, uCE, uBCE
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parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
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parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1 or cpu)')
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@ -369,7 +369,7 @@ def compute_loss(p, targets, model, giou_flag=True): # predictions, targets, mo
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tcls, tbox, indices, anchor_vec = build_targets(model, targets)
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h = model.hyp # hyperparameters
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arc = model.arc # # (default, uCE, uBCE) detection architectures
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red = 'sum' # Loss reduction (sum or mean)
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red = 'mean' # Loss reduction (sum or mean)
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# Define criteria
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BCEcls = nn.BCEWithLogitsLoss(pos_weight=ft([h['cls_pw']]), reduction=red)
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