Instructions to use TJKlein/CLIP-ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TJKlein/CLIP-ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="TJKlein/CLIP-ViT") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("TJKlein/CLIP-ViT") model = AutoModelForZeroShotImageClassification.from_pretrained("TJKlein/CLIP-ViT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download all_results.json from TJKlein/CLIP-ViT: direct link, hf CLI and curl.
- Browser
- Download file 167 Bytes
-
https://huggingface.co/TJKlein/CLIP-ViT/resolve/main/all_results.json
- Command line
-
hf download hf://TJKlein/CLIP-ViT/all_results.json
-
curl -L -o all_results.json https://huggingface.co/TJKlein/CLIP-ViT/resolve/main/all_results.json
167 Bytes
| { | |
| "epoch": 69.57, | |
| "train_loss": 0.2846686257049441, | |
| "train_runtime": 4604.0545, | |
| "train_samples_per_second": 7.95, | |
| "train_steps_per_second": 0.043 | |
| } |