Instructions to use optimum-internal-testing/tiny-random-metaclip_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use optimum-internal-testing/tiny-random-metaclip_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="optimum-internal-testing/tiny-random-metaclip_2") 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("optimum-internal-testing/tiny-random-metaclip_2") model = AutoModelForZeroShotImageClassification.from_pretrained("optimum-internal-testing/tiny-random-metaclip_2") - Notebooks
- Google Colab
- Kaggle
Upload processor
Browse files- preprocessor_config.json +3 -3
preprocessor_config.json
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{
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"crop_size": {
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"height":
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"width":
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"do_center_crop": true,
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"do_convert_rgb": true,
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"shortest_edge":
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}
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}
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{
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"crop_size": {
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"height": 30,
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"width": 30
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},
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"do_center_crop": true,
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"do_convert_rgb": true,
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"shortest_edge": 30
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}
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}
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