rfdetr-roadsign-agree1-large-noaug

This model is a fine-tuned version of Roboflow/rf-detr-large on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 15.1008
Map: 0.6845
Map 50: 0.7721
Map 75: 0.7575
Map Small: -1.0
Map Medium: 0.4507
Map Large: 0.6917
Mar 1: 0.8025
Mar 10: 0.8845
Mar 100: 0.8855
Mar Small: -1.0
Mar Medium: 0.7414
Mar Large: 0.8885
Map Bus Stop: 0.7874
Mar 100 Bus Stop: 0.9
Map Do Not Enter: 0.9318
Mar 100 Do Not Enter: 0.9471
Map Do Not Stop: 0.6666
Mar 100 Do Not Stop: 0.9
Map Do Not Turn L: 0.7939
Mar 100 Do Not Turn L: 0.9167
Map Do Not Turn R: 0.7398
Mar 100 Do Not Turn R: 0.975
Map Do Not U Turn: 0.4199
Mar 100 Do Not U Turn: 0.8889
Map Enter Left Lane: 0.5538
Mar 100 Enter Left Lane: 0.9
Map Green Light: 0.6233
Mar 100 Green Light: 0.75
Map Left Right Lane: 0.8308
Mar 100 Left Right Lane: 0.9231
Map No Parking: 0.6834
Mar 100 No Parking: 0.9286
Map Parking: 0.8022
Mar 100 Parking: 0.86
Map Ped Crossing: 0.8679
Mar 100 Ped Crossing: 0.9571
Map Ped Zebra Cross: 0.8567
Mar 100 Ped Zebra Cross: 0.9
Map Railway Crossing: 0.6538
Mar 100 Railway Crossing: 0.9286
Map Red Light: 0.5374
Mar 100 Red Light: 0.8105
Map Stop: 0.8247
Mar 100 Stop: 0.9467
Map T Intersection L: 0.6582
Mar 100 T Intersection L: 0.9444
Map Traffic Light: 0.5362
Mar 100 Traffic Light: 0.7571
Map U Turn: 0.7833
Mar 100 U Turn: 0.8857
Map Warning: 0.6425
Mar 100 Warning: 0.8471
Map Yellow Light: 0.1808
Mar 100 Yellow Light: 0.7286

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: cosine
lr_scheduler_warmup_steps: 0.05
num_epochs: 30

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 Bus Stop	Mar 100 Bus Stop	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
151.9024	1.0	142	32.8617	0.0001	0.0003	0.0	-1.0	0.0	0.0001	0.001	0.0085	0.024	-1.0	0.0	0.0286	0.0	0.0	0.0	0.0	0.0	0.0444	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	0.0011	0.0857	0.0003	0.06	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0429	0.0	0.1429	0.0	0.0	0.0001	0.1286
17.7572	2.0	284	24.1080	0.0001	0.0006	0.0	-1.0	0.0	0.0001	0.0042	0.0152	0.0384	-1.0	0.0	0.0389	0.0	0.0	0.0002	0.0412	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	0.0002	0.2857	0.0	0.0	0.0002	0.0429	0.0	0.0	0.0009	0.14	0.0	0.0	0.0001	0.0857	0.0	0.0	0.0015	0.2118	0.0	0.0
13.5777	3.0	426	17.9923	0.0016	0.0039	0.0007	-1.0	0.0	0.0016	0.0367	0.0931	0.1338	-1.0	0.0133	0.1386	0.0	0.0	0.0032	0.5353	0.0	0.0	0.0	0.0	0.0004	0.3125	0.0	0.0	0.0	0.0	0.0001	0.1	0.0	0.0	0.0013	0.0214	0.0	0.0	0.0116	0.6214	0.0	0.0	0.0	0.0	0.0	0.0105	0.0137	0.5267	0.0	0.0	0.0004	0.0929	0.0	0.0	0.0019	0.5882	0.0	0.0
11.8330	4.0	568	14.0392	0.0122	0.0213	0.0133	-1.0	0.0039	0.0124	0.2033	0.2794	0.315	-1.0	0.3771	0.3188	0.0	0.0	0.0451	0.8059	0.0022	0.1222	0.0	0.0	0.0101	0.825	0.066	0.5333	0.0029	0.66	0.008	0.4	0.0566	0.3692	0.0015	0.0643	0.0	0.0	0.0469	0.7857	0.0	0.0	0.0	0.0	0.0007	0.2053	0.0055	0.6867	0.0	0.0	0.0031	0.4929	0.0	0.0	0.0069	0.6647	0.0	0.0
10.9088	5.0	710	13.6745	0.0244	0.0333	0.0258	-1.0	0.0622	0.0229	0.4086	0.5208	0.5406	-1.0	0.4367	0.5434	0.0	0.0	0.1131	0.9059	0.0065	0.8667	0.0023	0.1667	0.0354	0.75	0.0131	0.7556	0.003	0.36	0.0038	0.595	0.0286	0.8615	0.0399	0.75	0.0	0.0	0.0508	0.8571	0.0207	0.84	0.039	0.7714	0.0157	0.5211	0.026	0.66	0.0068	0.1889	0.0152	0.6429	0.0	0.0	0.056	0.7882	0.0371	0.0714
10.1246	6.0	852	11.9059	0.0622	0.0828	0.0741	-1.0	0.0858	0.0684	0.5484	0.6825	0.7082	-1.0	0.6629	0.7038	0.0042	0.2333	0.208	0.9	0.0566	0.9222	0.0168	0.95	0.0223	0.8375	0.0296	0.8778	0.1579	0.96	0.154	0.655	0.0975	0.9231	0.0591	0.8143	0.0277	0.16	0.0365	0.8429	0.0334	0.74	0.0104	0.8286	0.0874	0.7474	0.0249	0.7933	0.0642	0.9222	0.039	0.6643	0.0	0.0	0.1664	0.8294	0.0112	0.2714
9.4682	7.0	994	12.3341	0.148	0.1856	0.1781	-1.0	0.1383	0.1584	0.6807	0.8274	0.848	-1.0	0.7086	0.8472	0.0405	0.7333	0.1252	0.9	0.3002	0.9333	0.2242	0.95	0.0426	0.9125	0.0695	0.9111	0.1116	0.96	0.2741	0.79	0.4248	0.9077	0.1842	0.85	0.0216	0.86	0.1419	0.9143	0.3032	0.9	0.0906	0.8571	0.0869	0.7421	0.1354	0.7867	0.0559	0.9	0.1034	0.7143	0.061	0.8143	0.2845	0.8706	0.0259	0.6
9.4100	8.0	1136	12.8237	0.2491	0.2943	0.2849	-1.0	0.1951	0.2588	0.731	0.8582	0.8653	-1.0	0.7395	0.862	0.3797	0.7667	0.4204	0.9294	0.1764	0.9556	0.7815	0.9167	0.2217	0.95	0.1045	0.9444	0.3205	0.88	0.3868	0.78	0.5481	0.8923	0.2353	0.85	0.0575	0.88	0.1889	0.9357	0.1812	0.94	0.0634	0.8429	0.0967	0.7684	0.1462	0.8667	0.1003	0.9333	0.2027	0.7429	0.2489	0.8714	0.3182	0.8824	0.0531	0.6429
8.8955	9.0	1278	13.2693	0.3449	0.4126	0.3968	-1.0	0.2869	0.3523	0.7483	0.8468	0.8585	-1.0	0.7267	0.8586	0.52	0.75	0.6347	0.9176	0.4669	0.9444	0.2139	0.9	0.3695	0.95	0.2043	0.8889	0.3279	0.9	0.495	0.78	0.5744	0.8769	0.469	0.8714	0.2278	0.92	0.194	0.9429	0.1745	0.92	0.1449	0.8286	0.2409	0.8	0.5924	0.88	0.1982	0.8778	0.1599	0.7643	0.4948	0.8429	0.4821	0.8294	0.0571	0.6429
8.7862	10.0	1420	13.3853	0.3916	0.4523	0.4402	-1.0	0.3731	0.3997	0.7733	0.8616	0.8677	-1.0	0.7324	0.8673	0.5851	0.7167	0.8643	0.9235	0.4982	0.9222	0.5316	0.95	0.4414	0.9375	0.3015	0.9	0.4607	0.96	0.5708	0.74	0.5458	0.9231	0.4491	0.9214	0.3866	0.86	0.2602	0.95	0.4156	0.88	0.1392	0.9571	0.2604	0.7579	0.5053	0.8733	0.1881	0.9222	0.1987	0.8143	0.239	0.8	0.2816	0.8412	0.0998	0.6714
8.3397	11.0	1562	13.7088	0.4522	0.5222	0.5106	-1.0	0.4679	0.461	0.7938	0.8698	0.8819	-1.0	0.7452	0.8861	0.7126	0.75	0.8573	0.9294	0.4449	0.9222	0.7509	0.95	0.4772	0.9625	0.1537	0.8667	0.3837	0.94	0.5807	0.76	0.7965	0.9308	0.5086	0.9143	0.4937	0.84	0.352	0.9643	0.5972	0.88	0.1705	0.9857	0.346	0.8053	0.6109	0.9067	0.1558	0.9	0.4238	0.7643	0.2343	0.9143	0.366	0.8471	0.0806	0.7857
7.9370	12.0	1704	13.8914	0.4761	0.5438	0.5294	-1.0	0.4947	0.4757	0.8	0.8747	0.8916	-1.0	0.7457	0.8947	0.6886	0.8167	0.9089	0.9412	0.4804	0.9667	0.6339	0.9333	0.3186	0.9625	0.2082	0.9333	0.2176	0.92	0.65	0.81	0.766	0.9077	0.6265	0.9429	0.4102	0.84	0.5417	0.95	0.6668	0.9	0.1959	0.9857	0.3033	0.7947	0.6644	0.9133	0.2093	0.9444	0.289	0.75	0.5987	0.8571	0.4173	0.9118	0.2018	0.7429
7.4761	13.0	1846	13.9790	0.5169	0.5925	0.5735	-1.0	0.5008	0.5231	0.8087	0.8867	0.8931	-1.0	0.7005	0.8985	0.6375	0.7667	0.8919	0.9353	0.4213	0.9444	0.7818	0.95	0.5409	0.95	0.3786	0.9444	0.3141	0.9	0.6295	0.77	0.8418	0.9231	0.5306	0.9	0.3769	0.88	0.4342	0.9643	0.7926	0.94	0.2766	0.9857	0.3853	0.7842	0.7604	0.9067	0.1879	0.9444	0.4333	0.7857	0.506	0.8857	0.4008	0.8941	0.3336	0.8
7.1662	14.0	1988	14.3093	0.5384	0.6213	0.6081	-1.0	0.5256	0.5387	0.8114	0.8958	0.9009	-1.0	0.721	0.9072	0.7578	0.9167	0.9098	0.9471	0.4333	0.9444	0.7761	0.9167	0.5267	0.9375	0.3232	0.9222	0.3677	0.92	0.6046	0.77	0.8341	0.9231	0.5773	0.9429	0.4458	0.9	0.4031	0.9571	0.6844	0.94	0.2046	0.9429	0.4213	0.7842	0.8036	0.9	0.2201	0.9333	0.5607	0.8071	0.5671	0.9286	0.4679	0.8706	0.4167	0.8143
7.1712	15.0	2130	14.1171	0.5689	0.6443	0.6281	-1.0	0.4952	0.5692	0.8006	0.892	0.9006	-1.0	0.7138	0.9073	0.7389	0.7833	0.9278	0.9412	0.4654	0.9667	0.9163	0.95	0.4667	0.95	0.4616	0.9556	0.3138	0.94	0.5629	0.74	0.8616	0.9231	0.6487	0.9429	0.4788	0.86	0.752	0.9786	0.8153	0.96	0.2412	0.9857	0.3166	0.8105	0.8035	0.9267	0.4169	0.9111	0.5084	0.8071	0.5379	0.8714	0.428	0.8941	0.2846	0.8143
6.7269	16.0	2272	14.1679	0.6027	0.6888	0.6705	-1.0	0.5405	0.6059	0.8171	0.8968	0.9001	-1.0	0.7567	0.9052	0.7275	0.7667	0.9377	0.9471	0.3194	0.9444	0.869	0.9167	0.7249	0.975	0.4045	0.9333	0.3805	0.94	0.6699	0.8	0.8662	0.9385	0.6768	0.9643	0.581	0.86	0.7159	0.9714	0.7891	0.92	0.3338	0.9857	0.4462	0.7789	0.8599	0.9067	0.2917	0.9333	0.547	0.8	0.6028	0.8571	0.5601	0.9059	0.353	0.8571
6.3449	17.0	2414	14.1885	0.613	0.6897	0.6707	-1.0	0.4953	0.619	0.8169	0.8957	0.9027	-1.0	0.7295	0.9091	0.7487	0.7833	0.9315	0.9529	0.4021	0.9556	0.9495	0.95	0.6428	0.975	0.3497	0.9444	0.41	0.92	0.661	0.78	0.852	0.9231	0.6554	0.95	0.7156	0.88	0.7742	0.9786	0.8163	0.92	0.3823	0.9857	0.4688	0.8263	0.854	0.9267	0.3352	0.9333	0.4812	0.7571	0.6273	0.8857	0.4997	0.9	0.3147	0.8286
6.3178	18.0	2556	14.3209	0.6391	0.7168	0.705	-1.0	0.5785	0.6408	0.8328	0.9027	0.9064	-1.0	0.7681	0.9069	0.8032	0.9667	0.9229	0.9412	0.5115	0.9556	0.9096	0.9333	0.691	0.975	0.3382	0.9444	0.4191	0.94	0.6591	0.755	0.8627	0.9231	0.7207	0.9357	0.6145	0.9	0.8331	0.9714	0.8239	0.94	0.3948	0.9714	0.5913	0.8053	0.8642	0.9533	0.441	0.9444	0.5625	0.8071	0.5176	0.8714	0.5832	0.8294	0.3575	0.7714
5.9675	19.0	2698	14.5537	0.652	0.7336	0.7177	-1.0	0.5107	0.6554	0.8216	0.8947	0.9006	-1.0	0.699	0.9028	0.7721	0.7833	0.9233	0.9353	0.6121	0.9333	0.92	0.95	0.7538	0.9875	0.4183	0.9444	0.5311	0.96	0.6082	0.715	0.847	0.9538	0.7269	0.9429	0.7274	0.92	0.7512	0.9571	0.7813	0.94	0.3315	0.9571	0.5407	0.8053	0.8402	0.9133	0.5508	0.9333	0.6015	0.7714	0.6084	0.9286	0.5576	0.8941	0.2892	0.7857
6.0040	20.0	2840	14.6292	0.6375	0.7159	0.6994	-1.0	0.4473	0.6447	0.8217	0.8993	0.9038	-1.0	0.7862	0.9067	0.7543	0.8	0.9265	0.9412	0.5416	0.9222	0.8835	0.9333	0.6725	0.9875	0.3505	0.9222	0.439	0.92	0.6328	0.755	0.8326	0.9308	0.7136	0.95	0.7481	0.92	0.8323	0.9643	0.8522	0.94	0.4389	0.9571	0.4798	0.7947	0.8199	0.9533	0.4633	0.9333	0.5616	0.8	0.5966	0.9	0.636	0.9118	0.212	0.8429
5.4589	21.0	2982	14.8683	0.676	0.7539	0.7376	-1.0	0.4478	0.6846	0.8181	0.8925	0.8935	-1.0	0.7057	0.8967	0.8151	0.8333	0.9426	0.9529	0.5209	0.9444	0.899	0.9333	0.5937	0.975	0.4961	0.9556	0.5427	0.94	0.618	0.745	0.8254	0.9231	0.7445	0.9571	0.7161	0.88	0.8827	0.9714	0.8567	0.9	0.8032	0.9571	0.469	0.7526	0.8451	0.9533	0.4897	0.9333	0.575	0.7786	0.6947	0.8571	0.6698	0.9059	0.1964	0.7143
5.4794	22.0	3124	14.8209	0.6729	0.7502	0.7313	-1.0	0.4581	0.6788	0.8197	0.9109	0.9135	-1.0	0.7786	0.9188	0.754	0.9	0.9263	0.9412	0.5868	0.9444	0.8452	0.95	0.7325	0.9875	0.495	0.9556	0.5801	0.94	0.6046	0.765	0.8208	0.9308	0.7897	0.9429	0.7617	0.88	0.8759	0.9786	0.8517	0.92	0.7279	0.9714	0.4985	0.8158	0.8433	0.9467	0.3521	0.9333	0.5414	0.7857	0.6726	0.9	0.6575	0.9235	0.214	0.8714
5.1854	23.0	3266	14.9806	0.6863	0.7658	0.7481	-1.0	0.4818	0.6909	0.8209	0.8953	0.8974	-1.0	0.7838	0.896	0.7655	0.8	0.9373	0.9471	0.6667	0.9333	0.8346	0.9333	0.8012	0.9875	0.4689	0.9444	0.5382	0.94	0.6307	0.755	0.8328	0.9462	0.7797	0.9643	0.8689	0.92	0.8717	0.9714	0.852	0.92	0.6062	0.9429	0.4835	0.7632	0.8458	0.9467	0.4504	0.9444	0.585	0.8	0.7446	0.9	0.6445	0.9	0.205	0.6857
4.9699	24.0	3408	14.9073	0.6865	0.7729	0.755	-1.0	0.5094	0.6892	0.8145	0.8944	0.8978	-1.0	0.7862	0.8999	0.7998	0.8833	0.9255	0.9353	0.7016	0.9222	0.8579	0.95	0.7848	1.0	0.459	0.9222	0.5312	0.9	0.635	0.745	0.8556	0.9385	0.7331	0.9429	0.8168	0.88	0.8757	0.9571	0.8586	0.92	0.5792	0.9286	0.53	0.8105	0.817	0.94	0.5394	0.9444	0.5736	0.7857	0.6952	0.8857	0.6386	0.9059	0.2076	0.7571
4.8681	25.0	3550	15.0810	0.6936	0.7775	0.7619	-1.0	0.4749	0.7006	0.8102	0.8951	0.8966	-1.0	0.7624	0.898	0.7719	0.9	0.9365	0.9412	0.7042	0.9111	0.8552	0.95	0.811	0.9875	0.4438	0.9222	0.5807	0.94	0.6307	0.725	0.8295	0.9154	0.7247	0.9429	0.8171	0.88	0.8786	0.9643	0.8718	0.92	0.6671	0.9429	0.534	0.8053	0.8245	0.94	0.5276	0.9444	0.5799	0.7857	0.7323	0.8714	0.6534	0.9118	0.1916	0.7286
4.7608	26.0	3692	15.0759	0.6818	0.7663	0.7565	-1.0	0.4707	0.6874	0.8053	0.8876	0.8909	-1.0	0.7452	0.891	0.7718	0.8667	0.9332	0.9529	0.6885	0.9222	0.7937	0.9167	0.7388	0.9875	0.4128	0.9	0.5357	0.94	0.6185	0.755	0.8417	0.9385	0.6902	0.9429	0.8026	0.9	0.877	0.9643	0.8569	0.92	0.6397	0.9286	0.5185	0.7789	0.8314	0.9267	0.6288	0.9444	0.5591	0.7643	0.7774	0.8714	0.6242	0.8882	0.1763	0.7
4.5169	27.0	3834	15.1649	0.6806	0.7686	0.7533	-1.0	0.5089	0.6844	0.806	0.889	0.8895	-1.0	0.7924	0.8898	0.7867	0.85	0.9338	0.9471	0.6803	0.9222	0.8001	0.9333	0.7766	1.0	0.429	0.9	0.5538	0.9	0.623	0.74	0.8368	0.9231	0.6396	0.9214	0.7978	0.92	0.8809	0.9571	0.8584	0.9	0.6101	0.9429	0.5453	0.7737	0.8166	0.94	0.5665	0.9444	0.5647	0.7643	0.7582	0.9	0.6182	0.9	0.2157	0.7
4.4554	28.0	3976	15.1190	0.6809	0.7684	0.7539	-1.0	0.4675	0.6863	0.8018	0.8874	0.8893	-1.0	0.7448	0.8909	0.7719	0.8833	0.9329	0.9412	0.6819	0.9222	0.7836	0.9	0.7398	0.975	0.4142	0.9111	0.5538	0.9	0.6179	0.73	0.8356	0.9231	0.6536	0.9286	0.751	0.9	0.8761	0.9643	0.8567	0.9	0.6525	0.9286	0.5335	0.8053	0.8313	0.9333	0.6371	0.9444	0.5498	0.7714	0.785	0.8857	0.6395	0.9	0.2011	0.7286
4.6058	29.0	4118	15.1376	0.6803	0.7685	0.7545	-1.0	0.4505	0.6869	0.8019	0.8872	0.8884	-1.0	0.7552	0.8909	0.7873	0.9	0.9318	0.9471	0.6666	0.9	0.7936	0.95	0.7398	0.975	0.4204	0.9	0.5652	0.9	0.6041	0.735	0.8346	0.9231	0.6651	0.9214	0.7664	0.88	0.8667	0.9571	0.8568	0.92	0.6539	0.9429	0.5348	0.7895	0.8246	0.9467	0.6303	0.9444	0.5504	0.7571	0.7851	0.8857	0.6432	0.8529	0.1659	0.7286
4.3633	30.0	4260	15.1008	0.6845	0.7721	0.7575	-1.0	0.4507	0.6917	0.8025	0.8845	0.8855	-1.0	0.7414	0.8885	0.7874	0.9	0.9318	0.9471	0.6666	0.9	0.7939	0.9167	0.7398	0.975	0.4199	0.8889	0.5538	0.9	0.6233	0.75	0.8308	0.9231	0.6834	0.9286	0.8022	0.86	0.8679	0.9571	0.8567	0.9	0.6538	0.9286	0.5374	0.8105	0.8247	0.9467	0.6582	0.9444	0.5362	0.7571	0.7833	0.8857	0.6425	0.8471	0.1808	0.7286

Framework versions

Transformers 5.12.1
Pytorch 2.12.1+cu130
Datasets 5.0.0
Tokenizers 0.22.2

merve/rfdetr-roadsign-agree1-large-noaug

rfdetr-roadsign-agree1-large-noaug

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Seed this model