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:
- 3cf35489679db06adccf0d001d82717df569adcf9f128f2d0e44e1f39181a625
- Size of remote file:
- 2.52 GB
- SHA256:
- 54355f819ab288b5878687779b56ce85dadac2ad0b8d3b165c401e933d2428f5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.