File size: 1,250 Bytes
a9c8c95 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | # 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()
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