Update README.md

README.md

@@ -1,16 +1,17 @@
-yolov8m_flying_objects_detection
+---
+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.
 
-
 Model Summary
 This model has been trained to identify the following classes:
 
-Drone (UAV copter, )
+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:
 
@@ -36,13 +37,14 @@ 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:
+The confusion matrix below shows the normalized detection accuracy across different classes. Key insights include:
 
-Drone Detection: 85% accurate, with occasional misclassifications as background.
+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% accurate, with some misclassifications as helicopters.
+Bird Detection: 68% accuracy, 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:
 
@@ -50,20 +52,16 @@ Airport Surveillance: Detecting drones and birds to prevent collisions and ensur
 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
@@ -84,11 +82,11 @@ This model is released under the MIT License. Feel free to use, modify, and dist
 Citation
 If you use this model in your work, please consider citing it as follows:
 
-less
+bibtex
 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}},
-}
+}