Update README.md

README.md

@@ -2,91 +2,101 @@
 license: mit
 pipeline_tag: object-detection
 ---
-yolov8m_flying_objects_detection is a deep learning model designed to detect various flying objects, including drones, airplanes, helicopters, and birds. Based on the YOLOv8 architecture, this model provides a strong balance of speed and accuracy, making it suitable for real-time aerial surveillance and monitoring applications.
+# yolov8m_flying_objects_detection
+`yolov8m_flying_objects_detection` is a deep learning model designed to detect various flying objects, including drones, airplanes, helicopters, and birds. Based on the YOLOv8 architecture, this model provides a strong balance of speed and accuracy, making it suitable for real-time aerial surveillance and monitoring applications.
 
-Model Summary
+## Model Summary
 This model has been trained to identify the following classes:
+- `Drone` (UAV copter)
+- `Airplane`
+- `Helicopter`
+- `Bird`
+- `Background` (no object)
 
-Drone (UAV copter)
-Airplane
-Helicopter
-Bird
-Background (no object)
-Classes and Objects
+## Classes and Objects
 The model has been trained to detect and classify the following types of flying objects:
 
-1. Drones
-DJI Matrice 200
-DJI Phantom 2
-DJI Phantom 3
-Shahed
-2. Airplanes
-Airbus A220
-Airbus A220 (with stowed landing gear)
-Airbus A380
-Boeing 787
-Boeing 787 (with stowed landing gear)
-3. Helicopters
-Bell 407
-Robinson R44
-4. Birds
-Chayka (Seagull)
-Golub (Pigeon)
-5. Background
-Areas with no relevant objects.
+1. **Drones**
+   - DJI Matrice 200
+   - DJI Phantom 2
+   - DJI Phantom 3
+   - Shahed
+2. **Airplanes**
+   - Airbus A220
+   - Airbus A220 (with stowed landing gear)
+   - Airbus A380
+   - Boeing 787
+   - Boeing 787 (with stowed landing gear)
+3. **Helicopters**
+   - Bell 407
+   - Robinson R44
+4. **Birds**
+   - Chayka (Seagull)
+   - Golub (Pigeon)
+5. **Background**
+   - Areas with no relevant objects.
+
 This breakdown provides more specific information on each class, helping users understand the diversity of objects the model can detect.
 
-Confusion Matrix Analysis
-The confusion matrix below shows the normalized detection accuracy across different classes. Key insights include:
+## Confusion Matrix Analysis
+The confusion matrix above shows the normalized detection accuracy across different classes. Key insights include:
 
-Drone Detection: 85% accuracy, with occasional misclassifications as background.
-Airplane Detection: Excellent accuracy of 99%.
-Helicopter Detection: Correctly identified 67% of the time, with some confusion with birds.
-Bird Detection: 68% accuracy, with some misclassifications as helicopters.
-Background: Some non-object areas are occasionally detected as objects.
+- **Drone Detection**: 85% accurate, with occasional misclassifications as background.
+- **Airplane Detection**: Excellent accuracy of 99%.
+- **Helicopter Detection**: Correctly identified 67% of the time, with some confusion with birds.
+- **Bird Detection**: 68% accurate, with some misclassifications as helicopters.
+- **Background**: Some non-object areas are occasionally detected as objects.
 
-Applications
+## Applications
 This model is particularly useful in scenarios where real-time identification of airborne objects is essential. Potential applications include:
+- **Airport Surveillance**: Detecting drones and birds to prevent collisions and ensure safety.
+- **Military and Security Operations**: Monitoring restricted airspaces for unauthorized drones or other aerial vehicles.
+- **Wildlife Monitoring**: Identifying bird movements to support ecological studies and prevent hazards.
 
-Airport Surveillance: Detecting drones and birds to prevent collisions and ensure safety.
-Military and Security Operations: Monitoring restricted airspaces for unauthorized drones or other aerial vehicles.
-Wildlife Monitoring: Identifying bird movements to support ecological studies and prevent hazards.
-Model Usage
-Install Dependencies
-Install the required packages listed in requirements.txt:
+## Model Usage
+To use the model, follow these steps:
 
-bash
+### Install Dependencies
+Install the required packages listed in `requirements.txt`:
+```bash
 pip install -r requirements.txt
-Run Inference
-Load the model and run inference on images or video frames using the sample inference.py script:
+```
 
-from yolov8 import YOLO
+## Run Inference
+Load the model and run inference on images or video frames using the sample `inference.py` script:
 
+```python
+from yolov8 import YOLO
 model = YOLO("yolov8m_fly_obj_detection.pt")
 results = model.predict("image.jpg")
-Output
+```
+
 The model outputs bounding boxes for each detected object, along with their respective class labels and confidence scores.
 
-Example Results
-Class	True Positive Rate	Common Misclassifications
-Drone	85%	Background
-Airplane	99%	None
-Helicopter	67%	Bird
-Bird	68%	Helicopter
-Limitations
-Class Confusion: Some confusion exists between similar classes (e.g., helicopters and birds).
-Background Misclassification: Non-object areas may occasionally be misclassified as objects.
-License
+## Example Results
+
+| Class      | True Positive Rate | Common Misclassifications |
+|------------|--------------------|---------------------------|
+| Drone      | 85%                | Background                 |
+| Airplane   | 99%                | None                       |
+| Helicopter | 67%                | Bird                       |
+| Bird       | 68%                | Helicopter                 |
+
+## Limitations
+- **Class Confusion**: Some confusion exists between similar classes (e.g., helicopters and birds).
+- **Background Misclassification**: Non-object areas may occasionally be misclassified as objects.
+
+## License
 This model is released under the MIT License. Feel free to use, modify, and distribute it, but please provide proper attribution.
 
-Citation
+## Citation
 If you use this model in your work, please consider citing it as follows:
 
-bibtex
-Копировать код
+```bibtex
 @model{yolov8m_flying_objects_detection,
   title={YOLOv8m Flying Object Detection},
   author={Javvanny},
   year={2024},
   howpublished={\url{https://huggingface.co/Javvanny/yolov8m_flying_objects_detection}},
-}
+}
+```