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
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model_name: Wheat Anomaly Detection Model
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tags:
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- pytorch
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- resnet
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- agriculture
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- anomaly-detection
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license: apache-2.0
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library_name:
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pipeline_tag: image-classification
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language:
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- en
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# Wheat Anomaly Detection Model
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model_name: Wheat Anomaly Detection Model
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tags:
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- pytorch
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- resnet
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- agriculture
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- anomaly-detection
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license: apache-2.0
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library_name: transformers
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datasets:
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- wheat-disease-dataset
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model_type: resnet
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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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framework: pytorch
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task: image-classification
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pipeline_tag: image-classification
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---
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# Wheat Anomaly Detection Model
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