Instructions to use Abhilashvj/CIRCL_website_classifier_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abhilashvj/CIRCL_website_classifier_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Abhilashvj/CIRCL_website_classifier_test") 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("Abhilashvj/CIRCL_website_classifier_test") model = AutoModelForImageClassification.from_pretrained("Abhilashvj/CIRCL_website_classifier_test") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cce0c4154d0c69d63c6ee809f38cc2538d3b6e71ac8743536b6d24bc32e4e7a5
- Size of remote file:
- 627 Bytes
- SHA256:
- 4cf093da31c7733407244481c89a6115a0bdc1e0fc43240dbdf23373234ae61e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.