Image Segmentation
ultralytics
Core ML
mask-generation
face-parsing
semantic-segmentation
yolo26
ios
on-device
celebamask-hq
Instructions to use a-ml/yolo26-face with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use a-ml/yolo26-face with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("a-ml/yolo26-face") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
File size: 547 Bytes
e2f3b24 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | # CelebAMask-HQ -> Ultralytics YOLO26 semantic segmentation
# masks: single-channel indexed PNG, pixel value == class index, 0 == background
path: /Users/ari/FaceSegmentation/dataset_celebamaskhq_semantic
train: images/train
val: images/val
test: images/test
masks_dir: masks
names:
0: background
1: skin
2: nose
3: eyeglasses
4: left_eye
5: right_eye
6: left_eyebrow
7: right_eyebrow
8: left_ear
9: right_ear
10: mouth
11: upper_lip
12: lower_lip
13: hair
14: hat
15: earring
16: necklace
17: neck
18: cloth
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