Zero-Shot Image Classification
OpenCLIP
PyTorch
clip
vision
language
histopathology
histology
medical
Instructions to use wisdomik/QuiltNet-B-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OpenCLIP
How to use wisdomik/QuiltNet-B-32 with OpenCLIP:
import open_clip model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:wisdomik/QuiltNet-B-32') tokenizer = open_clip.get_tokenizer('hf-hub:wisdomik/QuiltNet-B-32') - Notebooks
- Google Colab
- Kaggle
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## QuiltNet-B-32 Description
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QuiltNet-B-32 is a CLIP ViT-B/32 vision-language foundation model trained on the [Quilt-1M](https://quilt1m.github.io/) dataset curated from representative histopathology videos.
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It can perform various vision-language processing (VLP) tasks such as cross-modal retrieval, image classification, and visual question answering.
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QuiltNet establishes new state of the art in a wide range of standard datasets, and substantially outperforms prior VLP approaches:
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## QuiltNet-B-32 Description
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[QuiltNet-B-32](https://github.com/wisdomikezogwo/quilt1m/) is a CLIP ViT-B/32 vision-language foundation model trained on the [Quilt-1M](https://quilt1m.github.io/) dataset curated from representative histopathology videos.
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It can perform various vision-language processing (VLP) tasks such as cross-modal retrieval, image classification, and visual question answering.
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QuiltNet establishes new state of the art in a wide range of standard datasets, and substantially outperforms prior VLP approaches:
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