Image Feature Extraction
Transformers
Safetensors
mage_vit
feature-extraction
mage-vl
vision-encoder
codec-vit
video-understanding
custom_code
Instructions to use microsoft/Mage-ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/Mage-ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="microsoft/Mage-ViT", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("microsoft/Mage-ViT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Fix license references to MIT in model card body
Browse files
README.md
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<a href="https://github.com/microsoft/Mage"><img src="https://img.shields.io/badge/Code-GitHub-181717?logo=github" alt="GitHub" height="22"></a>
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<a href="https://huggingface.co/microsoft/Mage-VL"><img alt="Mage-VL" src="https://img.shields.io/badge/%F0%9F%A4%97-Mage--VL-yellow" height="22" /></a>
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<a href="https://huggingface.co/microsoft/Mage-ViT"><img alt="Mage-ViT" src="https://img.shields.io/badge/%F0%9F%A4%97-Mage--ViT-yellow" height="22" /></a>
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<a href="https://
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</p>
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| Pooling | learned-probe multi-head attention head |
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| Pre-training resolution | images 224–448 (variable), video 256 |
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| Weights dtype | bfloat16 |
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## 📄 License
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Released under the [
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<a href="https://github.com/microsoft/Mage"><img src="https://img.shields.io/badge/Code-GitHub-181717?logo=github" alt="GitHub" height="22"></a>
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<a href="https://huggingface.co/microsoft/Mage-VL"><img alt="Mage-VL" src="https://img.shields.io/badge/%F0%9F%A4%97-Mage--VL-yellow" height="22" /></a>
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<a href="https://huggingface.co/microsoft/Mage-ViT"><img alt="Mage-ViT" src="https://img.shields.io/badge/%F0%9F%A4%97-Mage--ViT-yellow" height="22" /></a>
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<a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-green" alt="License: MIT" height="22"></a>
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</p>
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---
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| Pooling | learned-probe multi-head attention head |
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| Pre-training resolution | images 224–448 (variable), video 256 |
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| Weights dtype | bfloat16 |
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| License | MIT |
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## 📄 License
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Released under the [MIT License](https://opensource.org/licenses/MIT).
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