Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing

  • Log In
  • Sign Up

nvidia
/
mit-b5

Image Classification
Transformers
PyTorch
google-tensorflow TensorFlow
segformer
vision
Model card Files Files and versions
xet
Community
2

Instructions to use nvidia/mit-b5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use nvidia/mit-b5 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-classification", model="nvidia/mit-b5")
    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-b5")
    model = AutoModelForImageClassification.from_pretrained("nvidia/mit-b5")
  • Inference
  • Notebooks
  • Google Colab
  • Kaggle
mit-b5
658 MB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 10 commits
nielsr's picture
nielsr HF Staff
Update README.md
4035715 almost 4 years ago
  • .gitattributes
    1.18 kB
    initial commit over 4 years ago
  • README.md
    3.35 kB
    Update README.md almost 4 years ago
  • config.json
    70 kB
    Remove reshape_last_stage about 4 years ago
  • preprocessor_config.json
    272 Bytes
    Update preprocessor_config.json over 4 years ago
  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch._utils._rebuild_tensor_v2",
    • "torch.FloatStorage",
    • "collections.OrderedDict"

    What is a pickle import?

    328 MB
    xet
    First commit over 4 years ago
  • tf_model.h5
    329 MB
    xet
    Add TF weights (#1) almost 4 years ago