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