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CLIP-ViT-B-32-256x256-DataComp-s34B-b86K-quant-ds

Zero-Shot Classification
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deepsparse
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  tags:
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  - deepsparse
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- This is a [SparseML](https://github.com/neuralmagic/sparseml) quantized version of https://huggingface.co/laion/CLIP-ViT-B-32-256x256-DataComp-s34B-b86K that is ready to use with [DeepSparse](https://github.com/neuralmagic/deepsparse). It achieves 71.1% one-shot accuracy on ImageNet and 95.6% one-shot accuracy on Imagenette.
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  Notebook for basic usage: [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1ZvU9ZSHJKSeJyH5bgxo_A-GSVIUcSt2E?usp=sharing)
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  Notebook for Imagenette evaluation: [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1-Duq0YNtjzOnmuXCYo-5DDiOzeCItXpN?usp=sharing)
 
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  tags:
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  - deepsparse
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+ This is a [SparseML](https://github.com/neuralmagic/sparseml) quantized version of https://huggingface.co/laion/CLIP-ViT-B-32-256x256-DataComp-s34B-b86K that is ready to use with [DeepSparse](https://github.com/neuralmagic/deepsparse). It achieves 71.1% zero-shot top-1 accuracy on ImageNet and 95.6% zero-shot top-1 accuracy on Imagenette.
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  Notebook for basic usage: [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1ZvU9ZSHJKSeJyH5bgxo_A-GSVIUcSt2E?usp=sharing)
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  Notebook for Imagenette evaluation: [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1-Duq0YNtjzOnmuXCYo-5DDiOzeCItXpN?usp=sharing)