Instructions to use OFA-Sys/chinese-clip-vit-base-patch16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OFA-Sys/chinese-clip-vit-base-patch16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="OFA-Sys/chinese-clip-vit-base-patch16") 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("OFA-Sys/chinese-clip-vit-base-patch16") model = AutoModelForZeroShotImageClassification.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from OFA-Sys/chinese-clip-vit-base-patch16: direct link, hf CLI and curl.
- Browser
- Download file 342 Bytes
-
https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://OFA-Sys/chinese-clip-vit-base-patch16/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16/resolve/main/preprocessor_config.json
342 Bytes
| { | |
| "do_center_crop": false, | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "ChineseCLIPFeatureExtractor", | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "resample": 3, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| } | |
| } |