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In this video, we cover:
~ The experiments involved in fine-tuning a model and evaluating the fine-tuned YOLOv9 model's performance and inference results.
~ Using the SkyFusion: Aerial Object Detection dataset with 3 labels (aircraft, ship, vehicle) and sample distributions.
~ Training the YOLOv9 compact model for 100 epochs to establish a baseline mAP50 score of 0.712.
~ Comparing YOLOv9 with YOLOv8 M model, achieving a 0.689 mAP50 score with YOLOv8, highlighting the superiority of YOLOv9.
~ Freezing the GELAN backbone (first 10 layers) in YOLOv9 and fine-tuning the model.
~ Experiments with freezing the backbone, adjusting the learning rate, and modifying the input image size.
~ Achieving an impressive mAP50 score of 0.716 by increasing the input image size to 1024 for better detection of small objects.
💡 What You’ll Learn:
~ The workflow for fine-tuning YOLOv9 on custom datasets.
~ Detailed insights into various experiments to optimize model performance.
~ Visualizing inference results and understanding the impact of different tuning strategies.
~ Practical applications and advantages of YOLOv9 in aerial object detection.
Watch our video on Learn OpenCV to dive into the implementation and fine-tuning experiments with the YOLOv9 model for aerial object detection.
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8 сен 2024
