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sam1120/
safety-utcustom-train-SF-RGBD-b5

Name: safety-utcustom-train-SF-RGBD-b5
Availability: InStock

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safety-utcustom-train-SF-RGBD-b5

This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set:

Loss: 0.0867
Mean Iou: 0.7280
Mean Accuracy: 0.7762
Overall Accuracy: 0.9818
Accuracy Unlabeled: nan
Accuracy Safe: 0.5578
Accuracy Unsafe: 0.9947
Iou Unlabeled: nan
Iou Safe: 0.4745
Iou Unsafe: 0.9814

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: 4e-06
train_batch_size: 15
eval_batch_size: 15
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.05
num_epochs: 120

Training results

Training Loss	Epoch	Step	Accuracy Safe	Accuracy Unlabeled	Accuracy Unsafe	Iou Safe	Iou Unlabeled	Iou Unsafe	Validation Loss	Mean Accuracy	Mean Iou	Overall Accuracy
0.789	0.91	10	0.0203	nan	0.8957	0.0095	0.0	0.8722	0.9555	0.4580	0.2939	0.8698
0.7579	1.82	20	0.0117	nan	0.9614	0.0069	0.0	0.9338	0.8322	0.4866	0.3136	0.9334
0.7103	2.73	30	0.0051	nan	0.9893	0.0043	0.0	0.9604	0.6729	0.4972	0.3216	0.9602
0.676	3.64	40	0.0021	nan	0.9969	0.0020	0.0	0.9675	0.5336	0.4995	0.3232	0.9675
0.5955	4.55	50	0.0001	nan	0.9993	0.0001	0.0	0.9698	0.4440	0.4997	0.3233	0.9698
0.5691	5.45	60	0.0000	nan	0.9997	0.0000	0.0	0.9702	0.3812	0.4999	0.3234	0.9702
0.5067	6.36	70	0.0	nan	0.9996	0.0	0.0	0.9701	0.3590	0.4998	0.3234	0.9701
0.4656	7.27	80	0.0	nan	0.9999	0.0	0.0	0.9703	0.3247	0.4999	0.3234	0.9703
0.4227	8.18	90	0.0	nan	0.9998	0.0	0.0	0.9702	0.3171	0.4999	0.3234	0.9702
0.3898	9.09	100	0.0004	nan	0.9996	0.0004	0.0	0.9701	0.3122	0.5000	0.3235	0.9701
0.3513	10.0	110	0.0	nan	0.9999	0.0	0.0	0.9703	0.2876	0.4999	0.3234	0.9703
0.4157	10.91	120	0.0000	nan	0.9998	0.0000	0.0	0.9703	0.2820	0.4999	0.3234	0.9703
0.3317	11.82	130	0.0	nan	0.9999	0.0	0.0	0.9703	0.2693	0.4999	0.3234	0.9703
0.321	12.73	140	0.0	nan	0.9999	0.0	0.0	0.9704	0.2647	0.4999	0.3235	0.9704
0.2887	13.64	150	0.0	nan	0.9999	0.0	0.0	0.9704	0.2539	0.5000	0.3235	0.9704
0.3008	14.55	160	0.0	nan	0.9999	0.0	0.0	0.9704	0.2536	0.5000	0.3235	0.9704
0.2853	15.45	170	0.0	nan	0.9999	0.0	0.0	0.9704	0.2397	0.5000	0.3235	0.9704
0.2684	16.36	180	0.0	nan	0.9999	0.0	0.0	0.9704	0.2321	0.5000	0.3235	0.9704
0.2585	17.27	190	0.0000	nan	0.9999	0.0000	0.0	0.9704	0.2208	0.5000	0.3235	0.9704
0.2088	18.18	200	0.0084	nan	0.9997	0.0083	0.0	0.9704	0.2011	0.5041	0.3262	0.9704
0.2518	19.09	210	0.0468	nan	0.9989	0.0451	0.0	0.9707	0.2026	0.5228	0.3386	0.9707
0.218	20.0	220	0.0879	nan	0.9984	0.0834	nan	0.9714	0.1889	0.5431	0.5274	0.9715
0.2046	20.91	230	0.1931	nan	0.9969	0.1752	nan	0.9730	0.1847	0.5950	0.5741	0.9732
0.2147	21.82	240	0.2042	nan	0.9968	0.1850	nan	0.9733	0.1766	0.6005	0.5791	0.9734
0.188	22.73	250	0.2020	nan	0.9972	0.1849	nan	0.9735	0.1726	0.5996	0.5792	0.9737
0.2175	23.64	260	0.1898	nan	0.9974	0.1748	nan	0.9734	0.1706	0.5936	0.5741	0.9735
0.2059	24.55	270	0.3006	nan	0.9962	0.2670	nan	0.9754	0.1689	0.6484	0.6212	0.9756
0.1776	25.45	280	0.2870	nan	0.9967	0.2587	nan	0.9755	0.1612	0.6418	0.6171	0.9757
0.1585	26.36	290	0.4254	nan	0.9944	0.3593	nan	0.9773	0.1537	0.7099	0.6683	0.9776
0.1588	27.27	300	0.2798	nan	0.9970	0.2548	nan	0.9756	0.1527	0.6384	0.6152	0.9758
0.153	28.18	310	0.4288	nan	0.9946	0.3646	nan	0.9776	0.1452	0.7117	0.6711	0.9779
0.1623	29.09	320	0.4401	nan	0.9945	0.3726	nan	0.9778	0.1442	0.7173	0.6752	0.9781
0.1603	30.0	330	0.4050	nan	0.9958	0.3562	nan	0.9781	0.1407	0.7004	0.6671	0.9784
0.1694	30.91	340	0.4585	nan	0.9948	0.3911	nan	0.9786	0.1343	0.7266	0.6849	0.9789
0.1585	31.82	350	0.3861	nan	0.9962	0.3433	nan	0.9779	0.1353	0.6912	0.6606	0.9782
0.1342	32.73	360	0.4963	nan	0.9939	0.4132	nan	0.9789	0.1338	0.7451	0.6961	0.9792
0.1358	33.64	370	0.5048	nan	0.9937	0.4182	nan	0.9789	0.1342	0.7493	0.6986	0.9793
0.1493	34.55	380	0.4809	nan	0.9946	0.4080	nan	0.9791	0.1297	0.7377	0.6936	0.9794
0.1435	35.45	390	0.5658	nan	0.9923	0.4518	nan	0.9794	0.1271	0.7791	0.7156	0.9797
0.1305	36.36	400	0.4157	nan	0.9968	0.3758	nan	0.9793	0.1225	0.7062	0.6776	0.9796
0.1496	37.27	410	0.5385	nan	0.9934	0.4420	nan	0.9796	0.1237	0.7659	0.7108	0.9799
0.1445	38.18	420	0.5763	nan	0.9924	0.4615	nan	0.9798	0.1207	0.7843	0.7206	0.9801
0.1307	39.09	430	0.4853	nan	0.9956	0.4244	nan	0.9803	0.1194	0.7404	0.7023	0.9806
0.1379	40.0	440	0.5722	nan	0.9922	0.4557	nan	0.9795	0.1174	0.7822	0.7176	0.9798
0.1202	40.91	450	0.5399	nan	0.9943	0.4544	nan	0.9805	0.1143	0.7671	0.7175	0.9809
0.1239	41.82	460	0.5580	nan	0.9932	0.4558	nan	0.9800	0.1150	0.7756	0.7179	0.9803
0.1183	42.73	470	0.4777	nan	0.9961	0.4236	nan	0.9805	0.1129	0.7369	0.7021	0.9808
0.1202	43.64	480	0.5933	nan	0.9928	0.4793	nan	0.9806	0.1119	0.7930	0.7300	0.9810
0.1276	44.55	490	0.5425	nan	0.9942	0.4561	nan	0.9806	0.1131	0.7683	0.7183	0.9809
0.1172	45.45	500	0.6272	nan	0.9898	0.4700	nan	0.9787	0.1135	0.8085	0.7244	0.9791
0.1288	46.36	510	0.4236	nan	0.9974	0.3898	nan	0.9802	0.1105	0.7105	0.6850	0.9804
0.1185	47.27	520	0.6035	nan	0.9914	0.4711	nan	0.9796	0.1130	0.7975	0.7254	0.9800
0.1045	48.18	530	0.5750	nan	0.9930	0.4679	nan	0.9804	0.1102	0.7840	0.7241	0.9807
0.1211	49.09	540	0.5812	nan	0.9929	0.4715	nan	0.9804	0.1069	0.7870	0.7260	0.9808
0.1206	50.0	550	0.5221	nan	0.9953	0.4528	nan	0.9811	0.1071	0.7587	0.7169	0.9814
0.1193	50.91	560	0.4956	nan	0.9961	0.4398	nan	0.9811	0.1053	0.7459	0.7105	0.9814
0.1116	51.82	570	0.5257	nan	0.9951	0.4528	nan	0.9809	0.1043	0.7604	0.7169	0.9812
0.1218	52.73	580	0.5936	nan	0.9922	0.4724	nan	0.9801	0.1078	0.7929	0.7262	0.9804
0.1284	53.64	590	0.5872	nan	0.9924	0.4696	nan	0.9801	0.1054	0.7898	0.7248	0.9804
0.096	54.55	600	0.5451	nan	0.9942	0.4580	nan	0.9806	0.1028	0.7697	0.7193	0.9809
0.1091	55.45	610	0.6014	nan	0.9917	0.4725	nan	0.9798	0.1022	0.7965	0.7261	0.9802
0.1068	56.36	620	0.4926	nan	0.9962	0.4374	nan	0.9810	0.1015	0.7444	0.7092	0.9813
0.106	57.27	630	0.5713	nan	0.9937	0.4731	nan	0.9809	0.1011	0.7825	0.7270	0.9812
0.1009	58.18	640	0.4512	nan	0.9969	0.4089	nan	0.9805	0.1028	0.7240	0.6947	0.9807
0.1018	59.09	650	0.6053	nan	0.9919	0.4779	nan	0.9801	0.1022	0.7986	0.7290	0.9805
0.1012	60.0	660	0.5167	nan	0.9949	0.4427	nan	0.9805	0.1016	0.7558	0.7116	0.9808
0.1052	60.91	670	0.5464	nan	0.9943	0.4604	nan	0.9808	0.0999	0.7703	0.7206	0.9811
0.1229	61.82	680	0.5706	nan	0.9939	0.4750	nan	0.9810	0.0993	0.7822	0.7280	0.9814
0.0963	62.73	690	0.5746	nan	0.9936	0.4754	nan	0.9809	0.0974	0.7841	0.7282	0.9813
0.1115	63.64	700	0.5239	nan	0.9955	0.4562	nan	0.9813	0.0974	0.7597	0.7187	0.9816
0.1025	64.55	710	0.5845	nan	0.9935	0.4813	nan	0.9811	0.0964	0.7890	0.7312	0.9814
0.0916	65.45	720	0.5493	nan	0.9947	0.4685	nan	0.9813	0.0962	0.7720	0.7249	0.9816
0.1055	66.36	730	0.5273	nan	0.9953	0.4571	nan	0.9812	0.0947	0.7613	0.7191	0.9815
0.1081	67.27	740	0.6093	nan	0.9919	0.4813	nan	0.9802	0.0964	0.8006	0.7308	0.9806
0.1039	68.18	750	0.5405	nan	0.9945	0.4573	nan	0.9807	0.0950	0.7675	0.7190	0.9811
0.106	69.09	760	0.5564	nan	0.9943	0.4682	nan	0.9810	0.0939	0.7753	0.7246	0.9813
0.0912	70.0	770	0.5377	nan	0.9949	0.4612	nan	0.9811	0.0936	0.7663	0.7212	0.9814
0.0951	70.91	780	0.5600	nan	0.9941	0.4689	nan	0.9809	0.0938	0.7771	0.7249	0.9813
0.0998	71.82	790	0.5573	nan	0.9944	0.4705	nan	0.9812	0.0928	0.7759	0.7258	0.9815
0.0889	72.73	800	0.5398	nan	0.9949	0.4628	nan	0.9812	0.0931	0.7674	0.7220	0.9815
0.0906	73.64	810	0.5151	nan	0.9958	0.4528	nan	0.9813	0.0928	0.7555	0.7171	0.9816
0.0911	74.55	820	0.5682	nan	0.9938	0.4722	nan	0.9809	0.0924	0.7810	0.7265	0.9812
0.0907	75.45	830	0.4864	nan	0.9965	0.4365	nan	0.9812	0.0929	0.7415	0.7089	0.9815
0.1117	76.36	840	0.5239	nan	0.9956	0.4576	nan	0.9814	0.0934	0.7598	0.7195	0.9817
0.0812	77.27	850	0.5279	nan	0.9956	0.4605	nan	0.9814	0.0915	0.7617	0.7210	0.9817
0.0888	78.18	860	0.5615	nan	0.9942	0.4720	nan	0.9811	0.0915	0.7778	0.7266	0.9814
0.09	79.09	870	0.5414	nan	0.9948	0.4628	nan	0.9811	0.0920	0.7681	0.7220	0.9814
0.1052	80.0	880	0.5866	nan	0.9932	0.4790	nan	0.9808	0.0917	0.7899	0.7299	0.9812
0.0867	80.91	890	0.5252	nan	0.9955	0.4573	nan	0.9813	0.0912	0.7603	0.7193	0.9816
0.0942	81.82	900	0.5091	nan	0.9959	0.4490	nan	0.9813	0.0925	0.7525	0.7152	0.9815
0.0917	82.73	910	0.5454	nan	0.9950	0.4682	nan	0.9814	0.0908	0.7702	0.7248	0.9817
0.103	83.64	920	0.5452	nan	0.9949	0.4672	nan	0.9813	0.0912	0.7701	0.7243	0.9816
0.0939	84.55	930	0.5539	nan	0.9947	0.4717	nan	0.9814	0.0900	0.7743	0.7265	0.9817
0.0892	85.45	940	0.5330	nan	0.9954	0.4635	nan	0.9815	0.0900	0.7642	0.7225	0.9818
0.0899	86.36	950	0.5756	nan	0.9938	0.4778	nan	0.9811	0.0905	0.7847	0.7295	0.9814
0.0877	87.27	960	0.5771	nan	0.9937	0.4787	nan	0.9811	0.0893	0.7854	0.7299	0.9814
0.0851	88.18	970	0.5087	nan	0.9961	0.4512	nan	0.9814	0.0897	0.7524	0.7163	0.9817
0.0857	89.09	980	0.5363	nan	0.9953	0.4644	nan	0.9814	0.0894	0.7658	0.7229	0.9817
0.0821	90.0	990	0.5333	nan	0.9953	0.4623	nan	0.9814	0.0895	0.7643	0.7218	0.9817
0.0931	90.91	1000	0.5581	nan	0.9944	0.4718	nan	0.9812	0.0895	0.7763	0.7265	0.9815
0.0787	91.82	1010	0.5525	nan	0.9946	0.4689	nan	0.9812	0.0889	0.7735	0.7251	0.9815
0.0865	92.73	1020	0.5659	nan	0.9941	0.4746	nan	0.9812	0.0883	0.7800	0.7279	0.9815
0.0939	93.64	1030	0.5583	nan	0.9945	0.4723	nan	0.9813	0.0891	0.7764	0.7268	0.9816
0.0874	94.55	1040	0.5258	nan	0.9955	0.4580	nan	0.9813	0.0893	0.7607	0.7197	0.9816
0.0927	95.45	1050	0.5319	nan	0.9953	0.4608	nan	0.9813	0.0894	0.7636	0.7211	0.9816
0.0808	96.36	1060	0.5444	nan	0.9949	0.4665	nan	0.9813	0.0897	0.7696	0.7239	0.9816
0.0924	97.27	1070	0.5445	nan	0.9950	0.4671	nan	0.9814	0.0892	0.7697	0.7243	0.9817
0.08	98.18	1080	0.5522	nan	0.9947	0.4703	nan	0.9813	0.0884	0.7735	0.7258	0.9816
0.0798	99.09	1090	0.0880	0.7300	0.7842	0.9815	nan	0.5745	0.9939	nan	0.4788	0.9812
0.0789	100.0	1100	0.0877	0.7231	0.7668	0.9817	nan	0.5383	0.9952	nan	0.4647	0.9814
0.0801	100.91	1110	0.0885	0.7232	0.7677	0.9816	nan	0.5404	0.9951	nan	0.4650	0.9813
0.1043	101.82	1120	0.0891	0.7242	0.7697	0.9816	nan	0.5445	0.9950	nan	0.4670	0.9813
0.0893	102.73	1130	0.0882	0.7263	0.7728	0.9817	nan	0.5508	0.9949	nan	0.4712	0.9814
0.0923	103.64	1140	0.0892	0.7134	0.7504	0.9815	nan	0.5048	0.9960	nan	0.4457	0.9812
0.0915	104.55	1150	0.0884	0.7293	0.7795	0.9817	nan	0.5646	0.9944	nan	0.4772	0.9814
0.0859	105.45	1160	0.0880	0.7340	0.7941	0.9815	nan	0.5949	0.9932	nan	0.4869	0.9811
0.0872	106.36	1170	0.0872	0.7298	0.7815	0.9817	nan	0.5688	0.9942	nan	0.4783	0.9814
0.0845	107.27	1180	0.0881	0.7310	0.7843	0.9817	nan	0.5746	0.9940	nan	0.4806	0.9813
0.0842	108.18	1190	0.0869	0.7285	0.7766	0.9818	nan	0.5584	0.9947	nan	0.4755	0.9815
0.0906	109.09	1200	0.0875	0.7277	0.7754	0.9818	nan	0.5560	0.9947	nan	0.4740	0.9815
0.0953	110.0	1210	0.0878	0.7289	0.7777	0.9818	nan	0.5608	0.9946	nan	0.4764	0.9815
0.0988	110.91	1220	0.0880	0.7303	0.7809	0.9818	nan	0.5674	0.9944	nan	0.4790	0.9815
0.0894	111.82	1230	0.0869	0.7300	0.7801	0.9818	nan	0.5657	0.9945	nan	0.4785	0.9815
0.0788	112.73	1240	0.0868	0.7283	0.7758	0.9818	nan	0.5569	0.9948	nan	0.4750	0.9815
0.0793	113.64	1250	0.0870	0.7281	0.7758	0.9818	nan	0.5569	0.9947	nan	0.4747	0.9815
0.084	114.55	1260	0.0874	0.7295	0.7809	0.9817	nan	0.5675	0.9943	nan	0.4777	0.9814
0.0832	115.45	1270	0.0875	0.7277	0.7760	0.9817	nan	0.5574	0.9946	nan	0.4739	0.9814
0.0833	116.36	1280	0.0873	0.7274	0.7755	0.9817	nan	0.5563	0.9947	nan	0.4735	0.9814
0.0786	117.27	1290	0.0867	0.7277	0.7754	0.9818	nan	0.5561	0.9947	nan	0.4740	0.9815
0.0839	118.18	1300	0.0865	0.7285	0.7779	0.9817	nan	0.5613	0.9945	nan	0.4755	0.9814
0.0847	119.09	1310	0.0877	0.7293	0.7816	0.9816	nan	0.5691	0.9941	nan	0.4773	0.9813
0.0933	120.0	1320	0.0867	0.7280	0.7762	0.9818	nan	0.5578	0.9947	nan	0.4745	0.9814

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3

sam1120/safety-utcustom-train-SF-RGBD-b5

safety-utcustom-train-SF-RGBD-b5

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sam1120/
safety-utcustom-train-SF-RGBD-b5