Instructions to use yujiepan/clip-vit-tiny-random-patch14-336 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yujiepan/clip-vit-tiny-random-patch14-336 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="yujiepan/clip-vit-tiny-random-patch14-336") 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("yujiepan/clip-vit-tiny-random-patch14-336") model = AutoModelForZeroShotImageClassification.from_pretrained("yujiepan/clip-vit-tiny-random-patch14-336", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from yujiepan/clip-vit-tiny-random-patch14-336: direct link, hf CLI and curl.
- Browser
- Download file 504 Bytes
-
https://huggingface.co/yujiepan/clip-vit-tiny-random-patch14-336/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://yujiepan/clip-vit-tiny-random-patch14-336/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/yujiepan/clip-vit-tiny-random-patch14-336/resolve/main/preprocessor_config.json
504 Bytes
| { | |
| "crop_size": { | |
| "height": 336, | |
| "width": 336 | |
| }, | |
| "do_center_crop": true, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "CLIPImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "processor_class": "CLIPProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "shortest_edge": 336 | |
| } | |
| } | |