Merge remote-tracking branch 'origin/master'
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Glenn Jocher
commit
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README.md
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README.md
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@ -61,6 +61,21 @@ 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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## Speed
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https://cloud.google.com/deep-learning-vm/
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**Machine type:** n1-highmem-4 (4 vCPUs, 26 GB memory)
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**CPU platform:** Intel Skylake
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**GPUs:** 1-4 x NVIDIA Tesla P100
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**HDD:** 100 GB SSD
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GPUs | `batch_size` | speed | COCO epoch
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--- |---| --- | ---
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(P100) | (images) | (s/batch) | (min/epoch)
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1 | 24 | 0.84s | 70min
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2 | 48 | 1.27s | 53min
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4 | 96 | 2.11s | 44min
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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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@ -77,18 +92,57 @@ Run `detect.py` with `webcam=True` to show a live webcam feed.
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# Pretrained Weights
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**Darknet** format:
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- https://pjreddie.com/media/files/yolov3.weights
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- https://pjreddie.com/media/files/yolov3-tiny.weights
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- Darknet `*.weights` format: https://pjreddie.com/media/files/yolov3.weights
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- PyTorch `*.pt` format: https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI
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**PyTorch** format:
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- https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI
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# mAP
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# Validation mAP
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- Use `test.py --weights weights/yolov3.weights` to test the official YOLOv3 weights.
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- Use `test.py --weights weights/latest.pt` to test the latest training results.
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- Compare to official darknet results from https://arxiv.org/abs/1804.02767.
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Run `test.py` to validate the official YOLOv3 weights `weights/yolov3.weights` against the 5000 validation images. You should obtain a .584 mAP at `--img-size 416`, or .586 at `--img-size 608` using this repo, compared to .579 at 608 x 608 reported in darknet (https://arxiv.org/abs/1804.02767).
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<i></i> | ultralytics/yolov3 | darknet
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--- | ---| ---
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YOLOv3-320 | 51.3 | 51.5
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YOLOv3-416 | 54.9 | 55.3
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YOLOv3-608 | 57.9 | 57.9
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Run `test.py --weights weights/latest.pt` to validate against the latest training results. **Default training settings produce a 0.522 mAP at epoch 62.** Hyperparameter settings and loss equation changes affect these results significantly, and additional trade studies may be needed to further improve this.
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``` bash
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sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
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# bash yolov3/data/get_coco_dataset.sh
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sudo rm -rf cocoapi && 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 --conf-thres 0.001 --img-size 416
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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=0.45, save_json=True, weights='weights/yolov3.weights')
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.308
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.549
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.310
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.334
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.454
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.267
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.403
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.428
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.585
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python3 test.py --save-json --conf-thres 0.001 --img-size 608 --batch-size 16
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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=0.45, save_json=True, weights='weights/yolov3.weights')
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.579
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.335
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.357
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.279
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.429
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.299
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.483
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.572
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```
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# Contact
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