| base_model: | |
| - openai/clip-vit-base-patch32 | |
| datasets: | |
| - cifar100 | |
| metrics: | |
| - accuracy | |
| # Model Card | |
| ## Model Details | |
| - Architecture: ViT-Base with patch size 32 | |
| - Training Data: cifar100 | |
| ## Training Details | |
| Adam Optimizer with a constant learning rate 1e-5 for 4000 steps training (batch_size=32). | |
| Only the vision encoder is fine-tuned. | |
| ## Evaluation Results | |
| - pre-trained: 0.6370000243186951 | |
| - fine-tuned: 0.8837000131607056 | |
| ## Usage | |
| load vision model | |
| ```python | |
| from transformers import CLIPVisionModel | |
| vision_model = CLIPVisionModel.from_pretrained('tanganke/clip-vit-base-patch32_cifar100') | |
| ``` | |
| substitute the vision encoder of clip | |
| ```python | |
| from transformers import CLIPModel | |
| clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") | |
| clip_model.vision_model.load_state_dict(vision_model.vision_model.state_dict()) | |
| ``` | |