A machine learning researcher has developed an arrow detection system for archery targets, achieving a 96.6% F1 score and 80.5% accuracy on test data. The system utilizes a ConvNeXt model pretrained on DINOv3, outperforming other architectures like ResNets and pure ViTs. The model was trained on a dataset of approximately 48,000 images, including both synthetic and manually labeled real-world archery targets, with a total of 1 million samples used during training with heavy augmentations. AI
IMPACT This research demonstrates a practical application of computer vision models for precise object detection, potentially improving automated scoring or analysis in sports.
RANK_REASON The item describes a specific research project and its results, including model performance and dataset details. [lever_c_demoted from research: ic=1 ai=1.0]
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