Duplicate from lxh1234-LXH/human-fall-detection

cd61f07 about 2 months ago

2.38 kB

---
license: agpl-3.0
library_name: ultralytics
pipeline_tag: object-detection
tags:
- yolo
- ultralytics
- yolov11
- object-detection
- fall-detection
- computer-vision
- safety
datasets:
- custom
---
![](https://huggingface.co/melihuzunoglu/human-fall-detection/resolve/main/sample_image.jpg)

# 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.

	---
	license: agpl-3.0
	library_name: ultralytics
	pipeline_tag: object-detection
	tags:
	- yolo
	- ultralytics
	- yolov11
	- object-detection
	- fall-detection
	- computer-vision
	- safety
	datasets:
	- custom
	---

	![](https://huggingface.co/melihuzunoglu/human-fall-detection/resolve/main/sample_image.jpg)

	# 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.