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---
base_model:
- openai/clip-vit-base-patch32
datasets:
- tanganke/stanford_cars
metrics:
- accuracy
---
# Model Card
## Model Details
- Architecture: ViT-Base with patch size 32
- Training Data: Standford Cars dataset
## 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: 0.5987
- fine-tuned: 0.7819
## Usage
load vision model
```python
from transformers import CLIPVisionModel
vision_model = CLIPVisionModel.from_pretrained('tanganke/clip-vit-base-patch32_stanford-cars')
```
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())
```
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