Instructions to use kd13/Modern-SqueezeNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kd13/Modern-SqueezeNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kd13/Modern-SqueezeNet", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("kd13/Modern-SqueezeNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update preprocessor_config.json
Browse files- preprocessor_config.json +2 -2
preprocessor_config.json
CHANGED
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@@ -9,7 +9,7 @@
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0.456,
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0.406
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],
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"image_processor_type": "
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"image_std": [
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0.229,
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0.224,
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"size": {
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"shortest_edge": 224
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}
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}
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0.456,
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0.406
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],
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"image_processor_type": "SqueezeNetImageProcessor",
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"image_std": [
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0.229,
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0.224,
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"size": {
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"shortest_edge": 224
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}
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+
}
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