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--- |
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base_model: |
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- openai/clip-vit-base-patch32 |
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datasets: |
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- mnist |
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metrics: |
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- accuracy |
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--- |
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# Model Card: tanganke/clip-vit-base-patch32_mnist |
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## Model Details |
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- Architecture: ViT-Base with patch size 32 |
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- Training Data: MNIST dataset |
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## Training Details |
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Adam Optimizer with a constant learning rate 1e-5 for 4000 steps training (batch_size=32). |
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Only the vision encoder is fine-tuned. |
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## Evaluation Results |
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- pre-trained: 0.4759327471256256 |
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- fine-tuned: 0.9957262277603149 |
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## Usage |
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load vision model |
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```python |
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from transformers import CLIPVisionModel |
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vision_model = CLIPVisionModel.from_pretrained('tanganke/clip-vit-base-patch32_mnist') |
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``` |
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substitute the vision encoder of clip |
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```python |
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from transformers import CLIPModel |
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clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") |
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clip_model.vision_model.load_state_dict(vision_model.vision_model.state_dict()) |
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``` |
