Instructions to use OpenGVLab/InternViT-6B-224px with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/InternViT-6B-224px with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="OpenGVLab/InternViT-6B-224px", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenGVLab/InternViT-6B-224px", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -28,6 +28,14 @@ It is _**the largest open-source vision/vision-language foundation model (14B)**
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- Image size: 224 x 224
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- **Pretrain Dataset:** LAION-en, LAION-COCO, COYO, CC12M, CC3M, SBU, Wukong, LAION-multi
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## Model Usage (Image Embeddings)
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```python
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- Image size: 224 x 224
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- **Pretrain Dataset:** LAION-en, LAION-COCO, COYO, CC12M, CC3M, SBU, Wukong, LAION-multi
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## Linear Probing Performance
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See this [document](https://github.com/OpenGVLab/InternVL/tree/main/classification) for more details about the linear probing evaluation.
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| IN-1K | IN-ReaL | IN-V2 | IN-A | IN-R | IN-Sketch |
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| :---: | :-----: | :---: | :--: | :--: | :-------: |
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| 88.2 | 90.4 | 79.9 | 77.5 | 89.8 | 69.1 |
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## Model Usage (Image Embeddings)
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```python
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