Update README.md

README.md

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----
-license: apache-2.0
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+---
+model_name: Wheat Anomaly Detection Model
+tags:
+  - pytorch
+  - resnet
+  - agriculture
+  - anomaly-detection
+  - image-classification
+  - wheat-disease-detection
+  - pest-detection
+  - agricultural-ai
+license: apache-2.0
+library_name: pytorch
+datasets:
+  - wheat-dataset   # Replace with the actual dataset name on Hugging Face if available
+model_type: resnet50
+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
+
+## Model Overview
+
+This model is trained to detect anomalies in wheat crops, such as pest infections (e.g., Fall Armyworm), diseases, or nutrient deficiencies. The model is based on the **ResNet50** architecture and was fine-tuned on a dataset of wheat images.
+
+## Model Details
+
+- **Model Architecture**: ResNet50
+- **Number of Classes**: 2 (Fall Armyworm, Healthy Wheat)
+- **Input Shape**: 224x224 pixels, 3 channels (RGB)
+- **Training Framework**: PyTorch
+- **Optimizer**: Adam
+- **Learning Rate**: 0.001
+- **Epochs**: 20
+- **Batch Size**: 32
+
+## Training
+
+The model was fine-tuned using a balanced dataset with images of healthy wheat and wheat infected by fall armyworms. The training involved transferring knowledge from a pretrained ResNet50 model and adjusting the final classification layer for the binary classification task.
+
+### Dataset
+
+The model was trained on a dataset hosted on Hugging Face. You can access it here:
+
+- **Dataset**: `your_huggingface_username/your_dataset_name`
+
+## How to Use
+
+To load and use this model in PyTorch, follow the steps below:
+
+### 1. Load the Model
+
+```python
+import torch
+import timm
+
+# Load the pre-trained model (fine-tuned ResNet50 for wheat anomaly detection)
+model = timm.create_model("resnet50", pretrained=False, num_classes=2)
+model.load_state_dict(torch.load("path_to_saved_model.pth"))
+model.eval()