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: cosine
lr_scheduler_warmup_steps: 0.05
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 Do Not Enter	Mar 100 Do Not Enter	Map Do Not Stop	Mar 100 Do Not Stop	Map Do Not Turn L	Mar 100 Do Not Turn L	Map Do Not Turn R	Mar 100 Do Not Turn R	Map Do Not U Turn	Mar 100 Do Not U Turn	Map Enter Left Lane	Mar 100 Enter Left Lane	Map Green Light	Mar 100 Green Light	Map Left Right Lane	Mar 100 Left Right Lane	Map No Parking	Mar 100 No Parking	Map Parking	Mar 100 Parking	Map Ped Crossing	Mar 100 Ped Crossing	Map Ped Zebra Cross	Mar 100 Ped Zebra Cross	Map Railway Crossing	Mar 100 Railway Crossing	Map Red Light	Mar 100 Red Light	Map Stop	Mar 100 Stop	Map T Intersection L	Mar 100 T Intersection L	Map Traffic Light	Mar 100 Traffic Light	Map U Turn	Mar 100 U Turn	Map Warning	Mar 100 Warning	Map Yellow Light	Mar 100 Yellow Light	Map Bus Stop	Mar 100 Bus Stop
24.1352	1.0	133	16.5739	0.0	0.0001	0.0	0.0	0.0	0.0	0.0004	0.0075	0.0419	0.0	0.0	0.0446	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0004	0.3444	0.0	0.0	0.0001	0.0333	0.0	0.0	0.0003	0.3889	0.0	0.0	0.0	0.0714	0.0	0.0	0.0	0.0	0.0	0.0	-1.0	-1.0
11.3730	2.0	266	13.0612	0.003	0.0096	0.0013	0.0	0.0007	0.003	0.0628	0.1418	0.1668	0.0	0.0261	0.1718	-1.0	-1.0	0.0071	0.0333	0.0	0.0	0.0064	0.15	0.0003	0.6	0.0148	0.5375	0.0046	0.0867	0.0003	0.0182	0.0	0.0	0.0006	0.0267	0.0	0.0	0.0067	0.6889	0.0	0.0	0.0025	0.2733	0.0004	0.0583	0.001	0.0222	0.0	0.0	0.0009	0.1571	0.0	0.0	0.0136	0.6846	0.0	0.0
8.8875	3.0	399	10.9539	0.0303	0.043	0.0309	0.0	0.0351	0.0399	0.2939	0.4263	0.4514	0.0	0.3194	0.4565	-1.0	-1.0	0.0146	0.275	0.0013	0.04	0.0129	0.875	0.0167	0.9	0.0623	0.8188	0.0108	0.8067	0.014	0.5591	0.0217	0.925	0.0152	0.72	0.0	0.0	0.1779	0.8389	0.0	0.0	0.0758	0.4667	0.0028	0.3667	0.0015	0.1667	0.0	0.0	0.0751	0.2	0.0	0.0	0.0522	0.8692	0.0505	0.2
8.0101	4.0	532	10.3960	0.0556	0.0749	0.0614	0.2322	0.0872	0.0564	0.5017	0.6379	0.6541	0.2667	0.3933	0.6667	-1.0	-1.0	0.0574	0.8583	0.1067	0.8733	0.0091	0.75	0.0105	0.9	0.0687	0.9187	0.1162	0.9067	0.1253	0.6409	0.0825	0.9875	0.0338	0.9	0.0109	0.2667	0.1453	0.9	0.0027	0.1	0.0589	0.6933	0.0208	0.475	0.1237	0.9111	0.0	0.0	0.0047	0.3	0.0021	0.35	0.1065	0.9	0.0267	0.45
7.6896	5.0	665	9.7700	0.1189	0.1398	0.1249	0.2738	0.2094	0.1176	0.7129	0.8363	0.8521	0.4667	0.5744	0.8531	-1.0	-1.0	0.0373	0.8833	0.0837	0.9333	0.0784	0.9	0.0696	0.9	0.2365	0.9438	0.1113	0.9533	0.2727	0.7545	0.3578	1.0	0.1671	0.88	0.0801	0.9	0.1694	0.9611	0.0164	0.9444	0.0878	0.8867	0.0426	0.5833	0.0788	0.9333	0.0208	0.7	0.032	0.3714	0.0165	0.8667	0.3041	0.9462	0.1146	0.8
7.2895	6.0	798	10.1798	0.1817	0.2111	0.2012	0.4419	0.2374	0.174	0.7467	0.876	0.8856	0.7	0.5694	0.8919	-1.0	-1.0	0.281	0.9	0.117	0.88	0.0341	0.925	0.0457	1.0	0.2552	0.9563	0.3588	0.9467	0.3751	0.7727	0.4468	1.0	0.1211	0.9533	0.3992	0.9	0.3758	0.9333	0.0244	0.9333	0.1971	0.8933	0.087	0.65	0.1371	0.9556	0.0342	0.95	0.0103	0.4929	0.0487	0.9	0.2468	0.9692	0.0385	0.8
7.1047	7.0	931	10.5961	0.2343	0.2785	0.2469	0.5509	0.3183	0.224	0.7346	0.8715	0.8775	0.6667	0.5267	0.8884	-1.0	-1.0	0.3472	0.9	0.2627	0.9133	0.0537	0.9	0.1563	0.95	0.203	0.925	0.3604	0.96	0.3856	0.8091	0.6891	0.9875	0.2315	0.94	0.404	0.9333	0.3475	0.9056	0.0602	0.9222	0.2107	0.96	0.0876	0.5833	0.2681	0.9556	0.0447	0.95	0.022	0.4929	0.0469	0.85	0.2312	0.9615	0.2744	0.75
6.7722	8.0	1064	10.8932	0.2772	0.3205	0.2946	0.7332	0.3556	0.2815	0.7724	0.8719	0.8835	0.7667	0.5444	0.8923	-1.0	-1.0	0.5526	0.9083	0.2559	0.9133	0.0974	0.9	0.604	1.0	0.3144	0.9563	0.4236	0.9467	0.3985	0.8045	0.4066	0.9625	0.2183	0.9267	0.3085	0.8667	0.3855	0.9278	0.1283	0.9444	0.3619	0.98	0.1817	0.75	0.1262	0.9333	0.1153	0.95	0.0181	0.5357	0.0779	0.8667	0.3141	0.9462	0.2555	0.65
6.6882	9.0	1197	11.2337	0.2837	0.3344	0.2945	0.6943	0.3481	0.28	0.7655	0.8737	0.8819	0.7667	0.545	0.8913	-1.0	-1.0	0.4763	0.925	0.205	0.9133	0.1515	0.9	0.5	1.0	0.3449	0.9375	0.4165	0.94	0.4026	0.7864	0.5967	1.0	0.1763	0.94	0.259	0.8	0.3273	0.9278	0.2502	0.9222	0.2404	0.96	0.175	0.7	0.2389	0.9333	0.0678	0.975	0.0455	0.55	0.1536	0.85	0.2937	0.9769	0.3521	0.7
6.5118	10.0	1330	11.2676	0.2815	0.3324	0.2924	0.7112	0.3387	0.2795	0.761	0.8776	0.8871	0.7333	0.56	0.8946	-1.0	-1.0	0.4958	0.9083	0.2217	0.9267	0.1535	0.875	0.3799	1.0	0.2976	0.9375	0.4876	0.96	0.3737	0.7818	0.5669	0.9875	0.2065	0.9333	0.3454	0.8333	0.3321	0.9333	0.1713	0.9556	0.2636	0.96	0.1826	0.6917	0.2961	0.9444	0.0455	0.975	0.0292	0.5214	0.1684	0.8833	0.2717	0.9846	0.3402	0.75

Framework versions

Transformers 5.12.1
Pytorch 2.12.1+cu130
Datasets 5.0.0
Tokenizers 0.22.2

merve/rfdetr-roadsign-agree2

rfdetr-roadsign-agree2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

merve/
rfdetr-roadsign-agree2