Instructions to use Luuu01/RESNETDONE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Luuu01/RESNETDONE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Luuu01/RESNETDONE") 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("Luuu01/RESNETDONE") model = AutoModelForImageClassification.from_pretrained("Luuu01/RESNETDONE") - Notebooks
- Google Colab
- Kaggle
Upload processor
Browse files- preprocessor_config.json +7 -3
preprocessor_config.json
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@@ -19,15 +19,19 @@
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.
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],
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"image_processor_type": "ConvNextImageProcessor",
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"image_std": [
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0.
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],
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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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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.485,
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0.456,
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0.406
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],
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"image_processor_type": "ConvNextImageProcessor",
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"image_std": [
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0.229,
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0.224,
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0.225
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],
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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": 224
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}
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}
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