YOLO26 Series Human Detection
Model Description
A human detection model trained on a custom-built dataset using the YOLO26 series.
Model Performance
Dataset
The dataset is aggregated from multiple sources, featuring images of various sizes and scenarios. Key statistics include:
- Total Images: 101,140
- Total Bounding Boxes: 875,283
- Diverse Scenario Coverage
- Sources: Public online datasets and custom-collected data.
Usage Scope
This model detects human positions within input images.
How to Use
- Inference: Input an image; the model returns bounding box coordinates for all detected humans. Supports multi-person detection.
Target Scenarios
- Human detection capabilities applicable to complex and diverse real-world scenarios.
Code Example
### Inference based on Ultralytics
### Install dependencies: pip install modelscope ultralytics
from pathlib import Path
from ultralytics import YOLO
modelfile = Path('/path/to/human-det-yolo26n.onnx')
model = YOLO(modelfile)
imgpath = '/path/to/you/image'
res = model.predict(imgpath)
for line in res:
print(line.boxes)
Training Process
- Trained for 100 epochs using Ultralytics YOLOv26-v8.4.95.
Evaluation Results
Objective metrics on the validation set:
| Model | Class | Validation Images | P |
|---|---|---|---|
| yolov26n | human | 5,677 | 0.93 |