Instructions to use Shubhamai/tiny-random-clip-zero-shot-image-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shubhamai/tiny-random-clip-zero-shot-image-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Shubhamai/tiny-random-clip-zero-shot-image-classification") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("Shubhamai/tiny-random-clip-zero-shot-image-classification") model = AutoModelForZeroShotImageClassification.from_pretrained("Shubhamai/tiny-random-clip-zero-shot-image-classification") - Notebooks
- Google Colab
- Kaggle
| { | |
| "crop_size": 30, | |
| "do_center_crop": true, | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "CLIPFeatureExtractor", | |
| "image_mean": [0.48145466, 0.4578275, 0.40821073], | |
| "image_std": [0.26862954, 0.26130258, 0.27577711], | |
| "resample": 3, | |
| "size": 30 | |
| } | |