| # Human-50e-11n | |
| ## Model Overview | |
| **Architecture:** YOLOv11 | |
| **Training Epochs:** 50 | |
| **Batch Size:** 32 | |
| **Optimizer:** auto | |
| **Learning Rate:** 0.0005 | |
| **Data Augmentation Level:** Moderate | |
| ## Training Metrics | |
| - **mAP@0.5:** 0.91583 | |
| ## Class IDs | |
| | Class ID | Class Name | | |
| |----------|------------| | |
| | 0 | Person | | |
| ## Datasets Used | |
| - detect-human-lg2ng_v1 | |
| - human-detection-grmvx_v1 | |
| - human-detection-p8c2v_v1 | |
| - human-pysi7_v3 | |
| - humans-ziarm_v2 | |
| - people-4evn7-fqlf8-d887c_v3 | |
| - people-4evn7_v2 | |
| - person-dataset-kzsop-vemv4-h1uoh-q5vtx_v2 | |
| - tello-olz2y_v5 | |
| ## Class Image Counts | |
| | Class Name | Image Count | | |
| |------------|-------------| | |
| | Person | 10865 | | |
| ## Description | |
| This model was trained using the YOLOv11 architecture on a custom dataset. The training process involved 50 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() | |