Instructions to use byrocuy/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use byrocuy/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="byrocuy/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("byrocuy/image_classification") model = AutoModelForImageClassification.from_pretrained("byrocuy/image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e2578f594d42b85301164b547a77695f9d043bfcf0a7d390236381b6e4064da
- Size of remote file:
- 4.09 kB
- SHA256:
- 69d18e4951286edf1b8f250de5c6f1970511e92cf4a674a46d71db002f199225
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.