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README.md

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+# rolo_yolo_fast2
+
+## Model Overview
+
+**Architecture:** YOLOv11  
+**Training Epochs:** 50  
+**Batch Size:** 16  
+**Optimizer:** auto  
+**Learning Rate:** 0.001  
+**Data Augmentation Level:** Basic
+
+## Training Metrics
+
+- **[email protected]:** 0.995
+
+## Class IDs
+
+| Class ID | Class Name |
+|----------|------------|
+| 0 | Panadol |
+| 1 | Revanin |
+
+
+## Datasets Used
+
+- Drug Classification
+
+## Class Image Counts
+
+| Class Name | Image Count |
+|------------|-------------|
+| Panadol | 116 |
+| Revanin | 122 |
+
+
+## Description
+
+This model was trained using the YOLOv11 architecture on a custom dataset. The training process involved 50 epochs with a batch size of 16. The optimizer used was **auto** with an initial learning rate of 0.001. Data augmentation was set to the **Basic** level to enhance model robustness.
+
+## Usage
+
+To use this model for inference, follow the instructions below:
+
+```python
+from ultralytics import YOLO
+
+# Load the trained model
+model = YOLO('best.pt')
+
+# Perform inference on an image
+results = model('path_to_image.jpg')
+
+# Display results
+results.show()