jays009
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Resnet3

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-model_name: Wheat Anomaly Detection Model - 3 Class
-tags:
-  - pytorch
-  - resnet50
-  - agriculture
-  - anomaly-detection
-  - wheat
-  - plant-disease
-license: apache-2.0
-library_name: transformers
-datasets:
-  - wheat-disease-dataset
-model_type: resnet50
-num_classes: 3
-classes:
-  - fall armyworm
-  - p_def
-  - blb
-preprocessing:
-  resize: 256
-  center_crop: 224
-  normalize:
-    - 0.485
-    - 0.456
-    - 0.406
-  normalize_std:
-    - 0.229
-    - 0.224
-    - 0.225
-framework: pytorch
-task: image-classification
-pipeline_tag: image-classification
-Wheat Anomaly Detection Model
-Overview
-This is a ResNet50-based image classification model designed for wheat anomaly detection. It classifies images into three categories:
-Fall Armyworm (fa)
-Phosphorus Deficiency (p_def)
-Bacterial Leaf Blight (blb)
-The model has been fine-tuned on an agricultural dataset and optimized for accurate detection of these anomalies.
-Model Performance
-Validation Accuracy: 93.82%
-Class-wise Accuracy:
-Fall Armyworm: 100.00%
-Phosphorus Deficiency: 86.21%
-Bacterial Leaf Blight: 95.00%
-Installation
-Ensure you have transformers, torch, and gradio installed:
-Usage
-Here is an example of how to load and use the model for prediction using PyTorch:
-Dataset
-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.
-License
-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.
-Citation
-If you use this model, please cite the repository as follows: