| # car-75e-11n | |
| ## Model Overview | |
| **Architecture:** YOLOv11 | |
| **Training Epochs:** 75 | |
| **Batch Size:** 32 | |
| **Optimizer:** auto | |
| **Learning Rate:** 0.0005 | |
| **Data Augmentation Level:** Moderate | |
| ## Training Metrics | |
| - **[email protected]:** 0.88072 | |
| ## Class IDs | |
| | Class ID | Class Name | | |
| |----------|------------| | |
| | 0 | Vehicle | | |
| ## Datasets Used | |
| - aerial-cars-rqcqh_v2 | |
| - bikedetection-7bpwy_v2 | |
| - car-detection-pyxz2_v4 | |
| - cars-bytt8_v35 | |
| - transport-rhkah_v8 | |
| - vehiclecount_v4 | |
| - vehicles-q0x2v-8kns4_v1 | |
| ## Class Image Counts | |
| | Class Name | Image Count | | |
| |------------|-------------| | |
| | Vehicle | 15163 | | |
| ## Description | |
| This model was trained using the YOLOv11 architecture on a custom dataset. The training process involved 75 epochs with a batch size of 32. The optimizer used was **auto** with an initial learning rate of 0.0005. Data augmentation was set to the **Moderate** 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() | |