Instructions to use nvidia/mit-b0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/mit-b0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nvidia/mit-b0") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("nvidia/mit-b0") model = AutoModelForImageClassification.from_pretrained("nvidia/mit-b0", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nvidia/mit-b0: direct link, hf CLI and curl.
- Browser
- Download file 14.4 MB
-
https://huggingface.co/nvidia/mit-b0/resolve/25ce79d97e6d9d509ed12e17cb2eb89b0a83a2dc/pytorch_model.bin
- Command line
-
hf download hf://nvidia/mit-b0@25ce79d97e6d9d509ed12e17cb2eb89b0a83a2dc/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nvidia/mit-b0/resolve/25ce79d97e6d9d509ed12e17cb2eb89b0a83a2dc/pytorch_model.bin
14.4 MB
- Xet hash:
- 04518abba51508be0afa7b132dde17998b41b54c3f93af4b8bf23eeacef7276f
- Size of remote file:
- 14.4 MB
- SHA256:
- 4af8348c2a802bf76115d34797ff5ce1d9f110bb8593c22d9b66d8bd7fa227fc
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