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model_name: Crop Anomaly Detection Model - 3 Class |
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tags: |
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- pytorch |
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- resnet50 |
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- agriculture |
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- anomaly-detection |
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- wheat |
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- plant-disease |
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license: apache-2.0 |
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library_name: transformers |
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datasets: |
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- Crop-disease-dataset |
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model_type: resnet50 |
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num_classes: 3 |
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classes: |
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- fall armyworm |
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- p_def |
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- blb |
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preprocessing: |
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resize: 256 |
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center_crop: 224 |
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normalize: |
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- 0.485 |
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- 0.456 |
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- 0.406 |
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normalize_std: |
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- 0.229 |
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- 0.224 |
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- 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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Overview |
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This is a ResNet50-based image classification model designed for wheat anomaly detection. It classifies images into three categories: |
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Fall Armyworm (fa) |
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Phosphorus Deficiency (p_def) |
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Bacterial Leaf Blight (blb) |
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The model has been fine-tuned on an agricultural dataset and optimized for accurate detection of these anomalies. |
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Model Performance |
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Validation Accuracy: 93.82% |
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Class-wise Accuracy: |
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Fall Armyworm: 100.00% |
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Phosphorus Deficiency: 86.21% |
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Bacterial Leaf Blight: 95.00% |
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Installation |
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Ensure you have transformers, torch, and gradio installed: |
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Usage |
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Here is an example of how to load and use the model for prediction using PyTorch: |
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Dataset |
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The model was trained using the Wheat Disease Dataset with balanced classes. Ensure your images are resized to 224x224 and normalized using the provided values. |
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License |
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This model is licensed under the Apache-2.0 License. You are free to use, modify, and distribute it under the terms of the license. |
