neuralmagic
/

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

Zero-Shot Classification

Model card Files Files and versions Community

mgoin commited on Dec 20, 2023

Commit

7610e80

•

1 Parent(s): 063a736

Update README.md

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -8,7 +8,7 @@ tags:
 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.
 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)
-Notebook for Imagenette evaluation: [![Colab](https://colab.research.google.com/drive/1-Duq0YNtjzOnmuXCYo-5DDiOzeCItXpN?usp=sharing)
 ## Setup for usage
 First, install DeepSparse with extensions for CLIP:

 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.
 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)
+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)
 ## Setup for usage
 First, install DeepSparse with extensions for CLIP: