Instructions to use hercapa/my_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hercapa/my_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hercapa/my_model") 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("hercapa/my_model") model = AutoModelForImageClassification.from_pretrained("hercapa/my_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cfae1770406a43946692dbe8db31b4d7678d5ccf2bfa3652c27948e024028d5a
- Size of remote file:
- 343 MB
- SHA256:
- b4bd5fa921bbd98e2b6eac6a396c9465dcb75e96905ea392412eecc6133573c3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.