Update README.md

README.md

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-license: agpl-3.0
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+---
+license: agpl-3.0
+---
+
+# 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
+
+# 1. Load the model from Hugging Face
+model = YOLO("melihuzunoglu/human-fall-detection")
+
+# 2. Run inference on an image or video
+# Replace 'your_video.mp4' with your actual file path
+results = model.predict(source="your_video.mp4", conf=0.25, save=True)
+
+# 3. View results
+for result in results:
+    result.show()
+```
+
+## 📊 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.