Instructions to use fptinters/DocClass-classify-model-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fptinters/DocClass-classify-model-V2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="fptinters/DocClass-classify-model-V2") 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("fptinters/DocClass-classify-model-V2") model = AutoModelForImageClassification.from_pretrained("fptinters/DocClass-classify-model-V2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 682d89c6971399bf3df67659289f4abc1928b194400073a79ffb7585410333a9
- Size of remote file:
- 343 MB
- SHA256:
- fc637a9f15d2bca98d171f7e9cbf8d7ebe356b798de16896a4665d9de3fc0899
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.