Instructions to use ILT37/Image-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ILT37/Image-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ILT37/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("ILT37/Image-classification") model = AutoModelForImageClassification.from_pretrained("ILT37/Image-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 43dd09be6154221196a9e41ec96e475f42e79c9b260f24a5f1001311bf9d4fef
- Size of remote file:
- 344 MB
- SHA256:
- de097412cad8746b6eb19ea1dc0e2199ef3d2a9a8accae460a9a51bf4e2f8331
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.