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README.md
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### RF-DETR with step learning rate scheduling and optimized hyperparameters
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### RF-DETR with step learning rate scheduling and optimized hyperparameters
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We fine-tuned RF-DETR using a step learning rate scheduler on a custom dataset. Within two epochs, the model achieved a `+3.7` increase in `mAP@50:95`, with balanced improvement in classification and localization losses. EMA weights consistently outperformed standard parameters, indicating stable convergence. Per-class analysis shows strong performance on well-represented categories like two-wheelers and trucks, while smaller or visually ambiguous classes such as minibuses remain challenging, suggesting future improvements via data balancing.
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## Total Loss over epochs
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## Per-class mAP@50:95
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## Per-class Precision and Recall (Last Epoch)
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## COCO mAP vs Epochs
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