Instructions to use nvidia/mit-b3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/mit-b3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nvidia/mit-b3") 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("nvidia/mit-b3") model = AutoModelForImageClassification.from_pretrained("nvidia/mit-b3") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 517735cfb989d35695af2162e40d9214fb0cf38c7fb4f7c5db212c631ac41c32
- Size of remote file:
- 179 MB
- SHA256:
- 5670aa22de1b1848b1d715cc97d8fef77761701dedc8b3d3e44525fd71012896
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.