2020-05-30 08:04:54 +08:00
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#!/bin/bash
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2020-07-05 07:25:48 +08:00
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# COCO 2017 dataset http://cocodataset.org
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# Download command: bash yolov5/data/get_coco2017.sh
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# Train command: python train.py --data ./data/coco.yaml
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# Dataset should be placed next to yolov5 folder:
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# /parent_folder
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# /coco
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# /yolov5
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2020-05-30 08:04:54 +08:00
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# Download labels from Google Drive, accepting presented query
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filename="coco2017labels.zip"
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fileid="1cXZR_ckHki6nddOmcysCuuJFM--T-Q6L"
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curl -c ./cookie -s -L "https://drive.google.com/uc?export=download&id=${fileid}" > /dev/null
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curl -Lb ./cookie "https://drive.google.com/uc?export=download&confirm=`awk '/download/ {print $NF}' ./cookie`&id=${fileid}" -o ${filename}
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rm ./cookie
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# Unzip labels
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unzip -q ${filename} # for coco.zip
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# tar -xzf ${filename} # for coco.tar.gz
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rm ${filename}
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# Download and unzip images
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cd coco/images
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f="train2017.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f # 19G, 118k images
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f="val2017.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f # 1G, 5k images
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# f="test2017.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f # 7G, 41k images
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# cd out
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cd ../..
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