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:
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()