Instructions to use jays009/Restnet50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jays009/Restnet50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jays009/Restnet50") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jays009/Restnet50", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +14 -3
config.json
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{
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"architectures": ["ResNet50"],
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"model_type": "resnet",
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"input_ids": "image",
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"task": "image-classification",
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"framework": "pytorch"
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}
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{
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"model_type": "resnet",
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"task": "image-classification",
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"framework": "pytorch",
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"pipeline_tag": "image-classification",
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"preprocessing": {
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"resize": 256,
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"center_crop": 224,
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"normalize": [0.485, 0.456, 0.406],
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"normalize_std": [0.229, 0.224, 0.225]
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},
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"license": "apache-2.0",
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"tags": [
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"image-classification",
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"wheat-disease-detection",
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"agriculture"
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]
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}
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