Instructions to use mulsi/fruit-vegetable-clip-vit-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mulsi/fruit-vegetable-clip-vit-base-patch32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mulsi/fruit-vegetable-clip-vit-base-patch32") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("mulsi/fruit-vegetable-clip-vit-base-patch32") model = AutoModelForImageClassification.from_pretrained("mulsi/fruit-vegetable-clip-vit-base-patch32") - Notebooks
- Google Colab
- Kaggle
Upload processor
Browse files- preprocessor_config.json +17 -0
preprocessor_config.json
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{
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"crop_size": {
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"height": 224,
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"width": 224
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{
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"_valid_processor_keys": [
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"images",
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"do_resize",
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"size",
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"resample",
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"do_center_crop",
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"crop_size",
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"do_rescale",
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"rescale_factor",
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"do_normalize",
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"image_mean",
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"image_std",
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"do_convert_rgb",
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"return_tensors",
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"data_format",
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"input_data_format"
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],
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"crop_size": {
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"height": 224,
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"width": 224
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