Update README.md

README.md

@@ -1,40 +1,31 @@
-model_name: Wheat Anomaly Detection Model
-tags:
-  - pytorch
-  - resnet
-  - agriculture
-  - anomaly-detection
-  - image-classification  # Add this tag to indicate it's an image classification model
-license: apache-2.0
-library_name: pytorch
-
----
 # Wheat Anomaly Detection Model
 
-This model is a PyTorch-based ResNet model trained to detect anomalies in wheat crops, such as diseases, pests, and nutrient deficiencies.
+## Overview
 
-## How to Load the Model
+This model is designed to detect anomalies in wheat crops, such as disease, pest infection, or nutrient deficiency. The model uses a ResNet50 architecture trained on a balanced dataset of healthy and anomalous wheat images. It is fine-tuned to detect various anomalies and is ideal for agricultural applications.
 
-To load the trained model, use the following code:
+## Model Details
 
-```python
-from transformers import AutoModelForImageClassification
-import torch
+- **Model Type**: ResNet50 (PyTorch)
+- **Task**: Image Classification / Anomaly Detection
+- **Intended Use**: Detect anomalies in wheat crops using images
+- **License**: Apache-2.0
 
-# Load the pre-trained model
-model = AutoModelForImageClassification.from_pretrained('your_huggingface_username/your_model_name')
+## Model Card Metadata
 
-# Put the model in evaluation mode
-model.eval()
+The following YAML configuration is part of the model's metadata to ensure correct pipeline identification on Hugging Face:
 
-# Example of making a prediction
-image_path = "path_to_your_image.jpg"  # Replace with your image path
-image = Image.open(image_path)
-inputs = transform(image).unsqueeze(0)  # Apply the necessary transformations to the image
-inputs = inputs.to(device)
-
-# Make a prediction
-with torch.no_grad():
-    outputs = model(inputs)
-    predicted_class = torch.argmax(outputs.logits, dim=1)
-    print(f"Predicted Class: {predicted_class.item()}")
+```yaml
+model_name: Wheat Anomaly Detection Model
+tags:
+  - pytorch
+  - resnet
+  - agriculture
+  - anomaly-detection
+  - image-classification
+  - timm
+license: apache-2.0
+library_name: pytorch
+datasets:
+  - your_huggingface_username/your_dataset_name  # Replace with your actual dataset name
+pipeline_tag: image-classification