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YOLOv8 Model Comparison & Fusion: Global Wheat Data Challenge | KerasCV
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Join us in this exciting tutorial where we dive into the world of object detection using YOLOv8 models with KerasCV. We take on the Global Wheat Challenge 2020 from Kaggle, utilizing YOLOv8 in its small, medium, and large variants. Our focus is not just on training these models but also on comparing their performance in detecting wheat heads from complex images.
After exploring each model's strengths and weaknesses, we take a step further by employing Weighted Boxes Fusion (WBF) to ensemble predictions from all three models. This technique enhances our overall detection accuracy by producing more efficient unified predictions.
Whether you're a machine learning enthusiast, a professional in the field, or just curious about object detection, this video will provide valuable insights into using YOLOv8 models and the practical implementation of WBF. Get ready to boost your knowledge and skills in object detection with KerasCV and YOLOv8!
Resources:
Processing, Image Classification, Object Detection, Face Detection, Face Recognition, YOLO, Segmentation, Pose Estimation, and many more using OpenCV(Python/C++), PyTorch, and TensorFlow.
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