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100 lines
1.9 KiB
YAML
100 lines
1.9 KiB
YAML
3 weeks ago
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
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# COCO128 dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
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# Example usage: python train.py --data coco128.yaml
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# parent
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# ├── yolov5
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# └── datasets
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# └── coco128 ← downloads here (7 MB)
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# 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, ..]
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path: ../datasets/coco128 # dataset root dir
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train: images/train2017 # train images (relative to 'path') 128 images
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val: images/train2017 # val images (relative to 'path') 128 images
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test: # test images (optional)
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# Classes
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names:
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0: love
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1: ok
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2: quiet
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# 3: motorcycle
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# 4: airplane
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# 5: bus
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# 6: train
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# 7: truck
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# 8: boat
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# 9: traffic light
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# 10: fire hydrant
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# 11: stop sign
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# 12: parking meter
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# 13: bench
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# 14: bird
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# 15: cat
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# 16: dog
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# 17: horse
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# 18: sheep
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# 19: cow
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# 20: elephant
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# 21: bear
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# 22: zebra
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# 23: giraffe
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# 24: backpack
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# 25: umbrella
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# 26: handbag
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# 27: tie
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# 28: suitcase
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# 29: frisbee
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# 30: skis
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# 31: snowboard
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# 32: sports ball
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# 33: kite
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# 34: baseball bat
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# 35: baseball glove
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# 36: skateboard
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# 37: surfboard
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# 38: tennis racket
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# 39: bottle
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# 40: wine glass
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# 41: cup
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# 42: fork
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# 43: knife
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# 44: spoon
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# 45: bowl
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# 46: banana
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# 47: apple
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# 48: sandwich
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# 49: orange
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# 50: broccoli
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# 51: carrot
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# 52: hot dog
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# 53: pizza
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# 54: donut
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# 55: cake
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# 56: chair
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# 57: couch
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# 58: potted plant
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# 59: bed
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# 60: dining table
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# 61: toilet
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# 62: tv
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# 63: laptop
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# 64: mouse
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# 65: remote
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# 66: keyboard
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# 67: cell phone
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# 68: microwave
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# 69: oven
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# 70: toaster
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# 71: sink
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# 72: refrigerator
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# 73: book
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# 74: clock
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# 75: vase
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# 76: scissors
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# 77: teddy bear
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# 78: hair drier
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# 79: toothbrush
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# Download script/URL (optional)
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#download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128.zip
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