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license: agpl-3.0
library_name: ultralytics
pipeline_tag: object-detection
tags:
- yolo
- ultralytics
- yolov11
- object-detection
- fall-detection
- computer-vision
- safety
datasets:
- custom
---
# Human Fall Detection with YOLOv11
This model is a specialized version of YOLOv11, fine-tuned to detect human falls in various environments. It is designed to provide real-time alerts for safety monitoring in elderly care facilities, hospitals, and industrial workplaces.
## 🚀 Quick Start (Usage)
You don't need to download the weights manually. You can load and run the model directly using the Python code below:
```python
from ultralytics import YOLO
from huggingface_hub import hf_hub_download
import os
model_path = hf_hub_download(repo_id="melihuzunoglu/human-fall-detection", filename="best.pt")
model = YOLO(model_path)
results = model.predict(source="image1.jpg", conf=0.25, save=True)
```
## ✅ Supported Classes (Labels)
The model can detect and distinguish between the following three states:
```python
Fallen: Active falling motion or a person on the ground after a fall.
Sitting: People sitting on chairs, benches, or floor.
Standing: People in an upright, standing position.
```
## 📊 Model Information
```python
Architecture: YOLOv11 (Ultralytics)
Task: Object Detection (Fall Detection)
Input Resolution: 640x640 pixels
Inference Speed: Optimized for real-time applications
```
## 🎯 Target Applications
```python
Elderly Safety: Automated fall detection for home or nursing home environments.
Occupational Health: Monitoring falls in hazardous work zones or construction sites.
Healthcare Support: Providing an extra layer of monitoring for patient rooms.
```
## 🛠 Training Details
The model was trained using the Ultralytics framework. The dataset was curated and pre-processed via Roboflow to ensure high accuracy and minimal false positives in common sitting or lying down scenarios.
## 👤 Developer
Author: Melih Uzunoğlu [Linkedin](https://www.linkedin.com/in/melih-uzunoglu/)
Framework: Ultralytics YOLOv11
Dataset Source: Roboflow
### Disclaimer
This model is developed for educational and research purposes. For critical safety implementations, it should be integrated with professional-grade monitoring systems. |