updates
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Glenn Jocher
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README.md
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README.md
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@ -43,7 +43,7 @@ Python 3.7 or later with the following `pip3 install -U -r requirements.txt` pac
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# Jupyter Notebook
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A jupyter notebook with training, detection and testing examples is available at:
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A jupyter notebook with training, inference and testing examples is available at:
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https://colab.research.google.com/drive/1G8T-VFxQkjDe4idzN8F-hbIBqkkkQnxw
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# Training
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@ -73,7 +73,7 @@ Reflection | 50% probability (horizontal-only)
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H**S**V Saturation | +/- 50%
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HS**V** Intensity | +/- 50%
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<img src="https://user-images.githubusercontent.com/26833433/50525037-6cbcbc00-0ad9-11e9-8c38-9fd51af530e0.jpg">
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<img src="https://user-images.githubusercontent.com/26833433/61579359-507b7d80-ab04-11e9-8a2a-bd6f59bbdfb4.jpg">
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## Speed
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@ -100,7 +100,7 @@ GPUs | `batch_size` | batch time | epoch time | epoch cost
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# Inference
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Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder:
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`detect.py` runs inference on all images **and videos** in the `data/samples` folder:
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**YOLOv3:** `python3 detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.weights`
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<img src="https://user-images.githubusercontent.com/26833433/50524393-b0adc200-0ad5-11e9-9335-4774a1e52374.jpg" width="600">
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@ -113,7 +113,7 @@ Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from
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## Webcam
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Run `detect.py` with `webcam=True` to show a live webcam feed.
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`detect.py` with `webcam=True` shows a live webcam feed.
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# Pretrained Weights
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@ -136,9 +136,9 @@ Success: converted 'weights/yolov3-spp.pt' to 'converted.weights'
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# mAP
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- `test.py --weights weights/yolov3.weights` to test official YOLOv3 weights.
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- `test.py --weights weights/last.pt` to test most recent checkpoint.
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- `test.py --weights weights/best.pt` to test best checkpoint.
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- `test.py --weights weights/yolov3.weights` tests official YOLOv3 weights.
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- `test.py --weights weights/last.pt` tests most recent checkpoint.
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- `test.py --weights weights/best.pt` tests best checkpoint.
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- Compare to darknet published results https://arxiv.org/abs/1804.02767.
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<!---
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@ -170,48 +170,45 @@ YOLOv3-608 | 57.9 (58.2) | 57.9
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`YOLOv3-spp 608` | 60.7 | 60.6
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``` bash
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git clone https://github.com/ultralytics/yolov3
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# bash yolov3/data/get_coco_dataset.sh
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# install pycocotools
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git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
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cd yolov3
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python3 test.py --save-json --img-size 416
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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')
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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 F1
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Calculating mAP: 100%|█████████████████████████████████████████| 157/157 [05:59<00:00, 1.71s/it]
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all 5e+03 3.58e+04 0.109 0.773 0.57 0.186
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.335
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.565
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.349
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.360
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.493
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.280
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.432
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.458
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.255
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.620
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python3 test.py --save-json --img-size 608 --batch-size 16
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Namespace(batch_size=16, 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')
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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 F1
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Computing mAP: 100%|█████████████████████████████████████████| 313/313 [06:11<00:00, 1.01it/s]
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all 5e+03 3.58e+04 0.12 0.81 0.611 0.203
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.366
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.607
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.386
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.207
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.391
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.485
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.296
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.464
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.494
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.331
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.517
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.618
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python3 test.py --save-json --img-size 608
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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')
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Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
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Class Images Targets P R mAP F1: 100% 313/313 [07:40<00:00, 2.34s/it]
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all 5e+03 3.58e+04 0.117 0.788 0.595 0.199
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.367
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.607 <--
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.387
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.208
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.392
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.297
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.465
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.495
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.332
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.518
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.621
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python3 test.py --save-json --img-size 416
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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/yolov3-spp.weights')
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Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
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Class Images Targets P R mAP F1: 100% 313/313 [07:01<00:00, 1.41s/it]
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all 5e+03 3.58e+04 0.105 0.746 0.554 0.18
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.336
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.565 <--
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.350
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.361
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.494
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.281
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.433
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.459
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.256
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.495
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.622
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```
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# Citation
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4
test.py
4
test.py
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@ -21,6 +21,7 @@ def test(cfg,
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# Initialize/load model and set device
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if model is None:
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device = torch_utils.select_device()
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verbose = False
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# Initialize model
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model = Darknet(cfg, img_size).to(device)
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@ -35,6 +36,7 @@ def test(cfg,
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model = nn.DataParallel(model)
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else:
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device = next(model.parameters()).device # get model device
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verbose = True
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# Configure run
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data = parse_data_cfg(data)
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@ -156,7 +158,7 @@ def test(cfg,
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print(pf % ('all', seen, nt.sum(), mp, mr, map, mf1))
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# Print results per class
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if nc > 1 and len(stats):
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if verbose and nc > 1 and len(stats):
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for i, c in enumerate(ap_class):
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print(pf % (names[c], seen, nt[c], p[i], r[i], ap[i], f1[i]))
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