Instructions to use touchtech/fashion-images-gender-age-vit-huge-patch14-224-in21k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use touchtech/fashion-images-gender-age-vit-huge-patch14-224-in21k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="touchtech/fashion-images-gender-age-vit-huge-patch14-224-in21k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("touchtech/fashion-images-gender-age-vit-huge-patch14-224-in21k") model = AutoModelForImageClassification.from_pretrained("touchtech/fashion-images-gender-age-vit-huge-patch14-224-in21k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13ff4aeeb1ed431233e7ec62039f88187387ed6143b85d1c1febf607510643c6
- Size of remote file:
- 4.22 kB
- SHA256:
- 21117d0565321e5fef969fb12183ca69adf57199c83f5aa813906b5aa7a67d03
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