| # 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() | |