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BiliSakura
/
MoCo-TP-ResNet-50

Image Classification
Transformers
Safetensors
PyTorch
moco_resnet
remote-sensing
resnet
self-supervised-learning
contrastive-learning
moco
custom_code
Model card Files Files and versions
xet
Community

Instructions to use BiliSakura/MoCo-TP-ResNet-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use BiliSakura/MoCo-TP-ResNet-50 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-classification", model="BiliSakura/MoCo-TP-ResNet-50", trust_remote_code=True)
    pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")
    # Load model directly
    from transformers import AutoModelForImageClassification
    model = AutoModelForImageClassification.from_pretrained("BiliSakura/MoCo-TP-ResNet-50", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
MoCo-TP-ResNet-50
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  • 1 contributor
History: 2 commits
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BiliSakura
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  • .gitattributes
    1.52 kB
    initial commit 3 months ago
  • README.md
    2.99 kB
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  • config.json
    557 Bytes
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  • configuration_moco.py
    731 Bytes
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  • model.safetensors
    94.3 MB
    xet
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  • modeling_moco.py
    11.2 kB
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  • modular_resnet.py
    13 kB
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