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
File size: 300 Bytes
8f01022 | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"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
}
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