# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license

# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics

# Example usage: python train.py --data coco128.yaml

# parent

# ├── yolov5

# └── datasets

#     └── coco128-seg  ← downloads here (7 MB)

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

path: ../datasets/coco128-seg  # dataset root dir

train: images/train2017  # train images (relative to 'path') 128 images

val: images/train2017  # val images (relative to 'path') 128 images

test:  # test images (optional)

# Classes

names:

  0: person

  1: bicycle

  2: car

  3: motorcycle

  4: airplane

  5: bus

  6: train

  7: truck

  8: boat

  9: traffic light

  10: fire hydrant

  11: stop sign

  12: parking meter

  13: bench

  14: bird

  15: cat

  16: dog

  17: horse

  18: sheep

  19: cow

  20: elephant

  21: bear

  22: zebra

  23: giraffe

  24: backpack

  25: umbrella

  26: handbag

  27: tie

  28: suitcase

  29: frisbee

  30: skis

  31: snowboard

  32: sports ball

  33: kite

  34: baseball bat

  35: baseball glove

  36: skateboard

  37: surfboard

  38: tennis racket

  39: bottle

  40: wine glass

  41: cup

  42: fork

  43: knife

  44: spoon

  45: bowl

  46: banana

  47: apple

  48: sandwich

  49: orange

  50: broccoli

  51: carrot

  52: hot dog

  53: pizza

  54: donut

  55: cake

  56: chair

  57: couch

  58: potted plant

  59: bed

  60: dining table

  61: toilet

  62: tv

  63: laptop

  64: mouse

  65: remote

  66: keyboard

  67: cell phone

  68: microwave

  69: oven

  70: toaster

  71: sink

  72: refrigerator

  73: book

  74: clock

  75: vase

  76: scissors

  77: teddy bear

  78: hair drier

  79: toothbrush

# Download script/URL (optional)

download: https://ultralytics.com/assets/coco128-seg.zip