---
license: apache-2.0
language:
- en
metrics:
- accuracy
pipeline_tag: object-detection
library_name: adapter-transformers
tags:
- lr_scheduler
- epochs:2
- cosine
---

### RF-DETR with cosine learning rate scheduling and optimized hyperparameters

We fine-tuned RF-DETR using a `cosine` learning rate scheduler to provide smoother optimization and improved late-stage refinement. The model demonstrates stable convergence with balanced reductions across classification, bounding box regression, and `GIoU` losses. EMA weights consistently outperform raw parameters, confirming reduced variance during training. The final model achieves `0.555 mAP@50:95`, with strong performance on well-represented vehicle classes such as two-wheelers, buses, and trucks. Remaining challenges are concentrated in visually ambiguous, low-frequency classes such as minibuses and vans.


Their is almost ~similar or no identical differences with `step vs cosine lr_scheduling`, it might be also because models are only finetuned for 2 epochs due to computational cost and disconnection in T4 GPU.

## Future goals

Retrain the models for around ~50 epochs one with cosine and one with step lr_scheduling to see the varying difference in smooth training and better convergence.