76807fae71
* YOLOv5 forward compatibility update * add data dir * ci test yolov3 * update build_targets() * update build_targets() * update build_targets() * update yolov3-spp.yaml * add yolov3-tiny.yaml * add yolov3-tiny.yaml * Update yolov3-tiny.yaml * thop bug fix * Detection() device bug fix * Use torchvision.ops.nms() * Remove redundant download mirror * CI tests with yolov3-tiny * Update README.md * Synch train and test iou_thresh * update requirements.txt * Cat apriori autolabels * Confusion matrix * Autosplit * Autosplit * Update README.md * AP no plot * Update caching * Update caching * Caching bug fix * --image-weights bug fix * datasets bug fix * mosaic plots bug fix * plot_study * boxes.max() * boxes.max() * boxes.max() * boxes.max() * boxes.max() * boxes.max() * update * Update README * Update README * Update README.md * Update README.md * results png * Update README * Targets scaling bug fix * update plot_study * update plot_study * update plot_study * update plot_study * Targets scaling bug fix * Finish Readme.md * Finish Readme.md * Finish Readme.md * Update README.md * Creado con Colaboratory
25 lines
935 B
Bash
Executable File
25 lines
935 B
Bash
Executable File
#!/bin/bash
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# COCO 2017 dataset http://cocodataset.org
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# Download command: bash data/scripts/get_coco.sh
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# Train command: python train.py --data coco.yaml
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# Default dataset location is next to /yolov3:
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# /parent_folder
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# /coco
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# /yolov3
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# Download/unzip labels
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d='../' # unzip directory
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url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
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f='coco2017labels.zip' # 68 MB
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echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
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# Download/unzip images
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d='../coco/images' # unzip directory
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url=http://images.cocodataset.org/zips/
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f1='train2017.zip' # 19G, 118k images
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f2='val2017.zip' # 1G, 5k images
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f3='test2017.zip' # 7G, 41k images (optional)
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for f in $f1 $f2; do
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echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
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done
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