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# Ultralytics YOLO π, AGPL-3.0 license | |
# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics | |
# Example usage: yolo train data=coco128.yaml | |
# parent | |
# βββ ultralytics | |
# βββ datasets | |
# βββ coco128-seg β 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 | |