Update README.md

README.md

@@ -1,3 +1,94 @@
----
-license: mit
----
+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
+This model has been trained to identify the following classes:
+
+Drone (UAV copter, )
+Airplane
+Helicopter
+Bird
+Background (no object)
+
+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.
+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 above shows the normalized detection accuracy across different classes. Key insights include:
+
+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
+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.
+Model Usage
+To use the model, follow these steps:
+
+Install Dependencies
+Install the required packages listed in requirements.txt:
+
+bash
+Копировать код
+pip install -r requirements.txt
+Run Inference
+Load the model and run inference on images or video frames using the sample inference.py script:
+
+python
+Копировать код
+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
+This model is released under the MIT License. Feel free to use, modify, and distribute it, but please provide proper attribution.
+
+Citation
+If you use this model in your work, please consider citing it as follows:
+
+less
+Копировать код
+@model{yolov8m_flying_objects_detection,
+  title={YOLOv8m Flying Object Detection},
+  author={Javvanny},
+  year={2024},
+  howpublished={\url{https://huggingface.co/Javvanny/yolov8m_flying_objects_detection}},
+}