KomdigiUB-Gambling-Detection-RTDETR-R50

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v3-rtdetr-r50-gambling-finetune

This model is a fine-tuned version of PekingU/rtdetr_r50vd_coco_o365 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 6.8580
• Map: 0.7154
• Map 50: 0.86
• Map 75: 0.7982
• Map Small: 0.4818
• Map Medium: 0.4823
• Map Large: 0.5059
• Mar 1: 0.6019
• Mar 10: 0.8463
• Mar 100: 0.876
• Mar Small: 0.8241
• Mar Medium: 0.8693
• Mar Large: 0.8723
• Map Banner Promo: 0.8704
• Mar 100 Banner Promo: 0.9604
• Map Cta Button: 0.7422
• Mar 100 Cta Button: 0.905
• Map Game Thumbnail: 0.6783
• Mar 100 Game Thumbnail: 0.9073
• Map Logo: 0.7334
• Mar 100 Logo: 0.848
• Map Menu Nav: 0.5527
• Mar 100 Menu Nav: 0.7593

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 300
• num_epochs: 10

Training results

Framework versions

• Transformers 5.0.0.dev0
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.1

BibTeX entry and citation info

@misc{lv2023detrs,
      title={DETRs Beat YOLOs on Real-time Object Detection},
      author={Yian Zhao and Wenyu Lv and Shangliang Xu and Jinman Wei and Guanzhong Wang and Qingqing Dang and Yi Liu and Jie Chen},
      year={2023},
      eprint={2304.08069},
      archivePrefix={arXiv},
      primaryClass={cs.CV}

@misc{rogge2025transformerstutorials,
  author       = {Rogge, Niels},
  title        = {Transformers Tutorials},
  year         = {2025},
  howpublished = {\url{https://github.com/NielsRogge/Transformers-Tutorials}}
}