Create README.md

README.md

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+---
+model_name: Crop Anomaly Detection Model - 3 Class
+tags:
+  - pytorch
+  - resnet50
+  - agriculture
+  - anomaly-detection
+  - wheat
+  - plant-disease
+license: apache-2.0
+library_name: transformers
+datasets:
+  - Crop-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.