Instructions to use hf-internal-testing/tiny-random-EfficientNetForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-EfficientNetForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-EfficientNetForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-EfficientNetForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-EfficientNetForImageClassification") - Notebooks
- Google Colab
- Kaggle
Update preprocessor_config.json
Browse files- preprocessor_config.json +4 -4
preprocessor_config.json
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{
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"crop_size": {
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"do_center_crop": false,
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"do_normalize": true,
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"rescale_factor": 0.00392156862745098,
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"rescale_offset": false,
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"size": {
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"height":
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"width":
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}
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{
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"crop_size": {
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"height": 64,
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"width": 64
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},
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"do_center_crop": false,
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"do_normalize": true,
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"rescale_factor": 0.00392156862745098,
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"rescale_offset": false,
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"size": {
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"height": 64,
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"width": 64
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}
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}
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