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data/coco.yaml

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+# 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
+
+
+# train and val datasets (image directory or *.txt file with image paths)
+train: ../coco/train2017.txt  # 118k images
+val: ../coco/val2017.txt  # 5k images
+test: ../coco/test-dev2017.txt  # 20k images for submission to https://competitions.codalab.org/competitions/20794
+
+# number of classes
+nc: 80
+
+# class names
+names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
+        'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+        'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
+        'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
+        'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+        'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
+        'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
+        'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
+        'hair drier', 'toothbrush']
+
+# Print classes
+# with open('data/coco.yaml') as f:
+#   d = yaml.load(f, Loader=yaml.FullLoader)  # dict
+#   for i, x in enumerate(d['names']):
+#     print(i, x)

data/coco128.yaml

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+# COCO 2017 dataset http://cocodataset.org - first 128 training images
+# Download command:  python -c "from yolov5.utils.google_utils import gdrive_download; gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f','coco128.zip')"
+# Train command: python train.py --data ./data/coco128.yaml
+# Dataset should be placed next to yolov5 folder:
+#   /parent_folder
+#     /coco128
+#     /yolov5
+
+
+# train and val datasets (image directory or *.txt file with image paths)
+train: ../coco128/images/train2017/
+val: ../coco128/images/train2017/
+
+# number of classes
+nc: 80
+
+# class names
+names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
+        'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+        'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
+        'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
+        'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+        'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
+        'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
+        'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
+        'hair drier', 'toothbrush']

data/get_coco2017.sh

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+#!/bin/bash
+# Zip coco folder
+# zip -r coco.zip coco
+# tar -czvf coco.tar.gz coco
+
+# 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 ../..