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

Training Loss	Epoch	Step	Validation Loss	Map	Map 50	Map 75	Map Small	Map Medium	Map Large	Mar 1	Mar 10	Mar 100	Mar Small	Mar Medium	Mar Large	Map Banner Promo	Mar 100 Banner Promo	Map Cta Button	Mar 100 Cta Button	Map Game Thumbnail	Mar 100 Game Thumbnail	Map Logo	Mar 100 Logo	Map Menu Nav	Mar 100 Menu Nav
No log	1.0	107	20.5763	0.0922	0.1237	0.1082	0.1056	0.0185	0.2108	0.1468	0.331	0.4918	0.301	0.4038	0.5959	0.399	0.9118	0.0195	0.4808	0.0367	0.5013	0.0013	0.2806	0.0046	0.2846
No log	2.0	214	9.0251	0.5324	0.6626	0.6192	0.3219	0.4183	0.6377	0.462	0.7685	0.8228	0.7076	0.7962	0.8858	0.7688	0.9316	0.6739	0.8749	0.5019	0.8567	0.4994	0.7971	0.2179	0.6538
No log	3.0	321	7.7306	0.6155	0.7736	0.6929	0.3994	0.4662	0.7004	0.5238	0.796	0.8365	0.6789	0.8151	0.8954	0.831	0.9507	0.672	0.8703	0.6238	0.8584	0.6296	0.7878	0.3213	0.7154
No log	4.0	428	7.1577	0.6898	0.8521	0.7639	0.4178	0.5517	0.8467	0.5838	0.813	0.8497	0.7595	0.8232	0.9338	0.8822	0.9676	0.7291	0.8863	0.6569	0.8385	0.6631	0.8165	0.5179	0.7396
29.4032	5.0	535	6.8177	0.7202	0.8795	0.7981	0.4606	0.552	0.8828	0.5851	0.83	0.8689	0.7771	0.8316	0.9249	0.9107	0.9699	0.743	0.9005	0.6694	0.8797	0.665	0.8266	0.6131	0.7681
29.4032	6.0	642	6.4833	0.7517	0.9161	0.8315	0.4888	0.6263	0.9139	0.5966	0.8393	0.877	0.7423	0.8503	0.9425	0.9318	0.975	0.792	0.9114	0.721	0.8935	0.6903	0.8216	0.6235	0.7835
29.4032	7.0	749	6.7079	0.7452	0.9086	0.8268	0.4626	0.5953	0.9113	0.5999	0.8343	0.8751	0.7833	0.8383	0.9446	0.927	0.9691	0.7828	0.9087	0.7239	0.8987	0.6797	0.8144	0.6126	0.7846
29.4032	8.0	856	6.7679	0.743	0.8989	0.8209	0.4715	0.6187	0.9201	0.6016	0.8354	0.8729	0.7509	0.837	0.9499	0.9227	0.9699	0.78	0.91	0.7224	0.8909	0.6738	0.8158	0.616	0.778
29.4032	9.0	963	6.5281	0.7457	0.8999	0.8317	0.4659	0.6117	0.9221	0.5949	0.8343	0.8674	0.69	0.8338	0.9453	0.9264	0.9706	0.7669	0.9023	0.7098	0.8719	0.6927	0.8129	0.6326	0.7791
9.6433	10.0	1070	6.5610	0.7537	0.9065	0.8402	0.4672	0.6283	0.926	0.6029	0.8364	0.8735	0.7052	0.8386	0.9521	0.9314	0.9743	0.7786	0.9082	0.7256	0.8792	0.6937	0.8158	0.6392	0.7901

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

bibtex

@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}

bibtex

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

iqqy-x/rtdetr-r50-gambling-finetune

v3-rtdetr-r50-gambling-finetune

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

BibTeX entry and citation info

iqqy-x/
rtdetr-r50-gambling-finetune