Instructions to use gitfreder/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gitfreder/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="gitfreder/image_classification") 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("gitfreder/image_classification") model = AutoModelForImageClassification.from_pretrained("gitfreder/image_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9cc415dea229ddead73c8c3c6dc17d104d965b59690022d7887ddf299651cb7a
- Size of remote file:
- 343 MB
- SHA256:
- bfcf1c600654078c831a7be8679d00a650213f071346f128f0f9515f9e9fe32f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.