Instructions to use tanganke/clip-vit-large-patch14_sun397 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tanganke/clip-vit-large-patch14_sun397 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tanganke/clip-vit-large-patch14_sun397")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("tanganke/clip-vit-large-patch14_sun397") model = AutoModel.from_pretrained("tanganke/clip-vit-large-patch14_sun397") - Notebooks
- Google Colab
- Kaggle
metadata
base_model:
- openai/clip-vit-large-patch14
datasets:
- tanganke/sun397
metrics:
- accuracy
Model Card
Model Details
- Architecture: ViT-Large with patch size 14
- Training Data: SUN397 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: 0.6830110549926758
- fine-tuned: 0.8275973796844482