segformer-b5-p142-cvat-vgs

This model is a fine-tuned version of nvidia/mit-b5 on the vigneshgs7/segformer_open_cv_RGB_L_0_1 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0131
• Mean Iou: 0.4961
• Mean Accuracy: 0.9922
• Overall Accuracy: 0.9922
• Accuracy Background: nan
• Accuracy Object: 0.9922
• Iou Background: 0.0
• Iou Object: 0.9922

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: 1e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Object	Iou Background	Iou Object
0.2847	0.06	20	0.3843	0.4662	0.9324	0.9324	nan	0.9324	0.0	0.9324
0.1681	0.11	40	0.1983	0.4704	0.9408	0.9408	nan	0.9408	0.0	0.9408
0.1592	0.17	60	0.1303	0.4745	0.9489	0.9489	nan	0.9489	0.0	0.9489
0.1177	0.23	80	0.0922	0.4944	0.9888	0.9888	nan	0.9888	0.0	0.9888
0.062	0.29	100	0.0745	0.4946	0.9892	0.9892	nan	0.9892	0.0	0.9892
0.0767	0.34	120	0.0545	0.4852	0.9703	0.9703	nan	0.9703	0.0	0.9703
0.0984	0.4	140	0.0621	0.4938	0.9875	0.9875	nan	0.9875	0.0	0.9875
0.1779	0.46	160	0.0504	0.4961	0.9921	0.9921	nan	0.9921	0.0	0.9921
0.0468	0.52	180	0.0407	0.4904	0.9807	0.9807	nan	0.9807	0.0	0.9807
0.0618	0.57	200	0.0390	0.4936	0.9873	0.9873	nan	0.9873	0.0	0.9873
0.062	0.63	220	0.0348	0.4947	0.9894	0.9894	nan	0.9894	0.0	0.9894
0.0357	0.69	240	0.0341	0.4914	0.9828	0.9828	nan	0.9828	0.0	0.9828
0.0304	0.74	260	0.0351	0.4960	0.9920	0.9920	nan	0.9920	0.0	0.9920
0.0267	0.8	280	0.0311	0.4938	0.9877	0.9877	nan	0.9877	0.0	0.9877
0.0536	0.86	300	0.0282	0.4904	0.9807	0.9807	nan	0.9807	0.0	0.9807
0.049	0.92	320	0.0274	0.4928	0.9855	0.9855	nan	0.9855	0.0	0.9855
0.0304	0.97	340	0.0262	0.4936	0.9872	0.9872	nan	0.9872	0.0	0.9872
0.0232	1.03	360	0.0251	0.4923	0.9847	0.9847	nan	0.9847	0.0	0.9847
0.0304	1.09	380	0.0240	0.4917	0.9835	0.9835	nan	0.9835	0.0	0.9835
0.0451	1.15	400	0.0261	0.4964	0.9927	0.9927	nan	0.9927	0.0	0.9927
0.0254	1.2	420	0.0234	0.4929	0.9859	0.9859	nan	0.9859	0.0	0.9859
0.0354	1.26	440	0.0229	0.4931	0.9861	0.9861	nan	0.9861	0.0	0.9861
0.2103	1.32	460	0.0224	0.4951	0.9902	0.9902	nan	0.9902	0.0	0.9902
0.041	1.38	480	0.0222	0.4920	0.9839	0.9839	nan	0.9839	0.0	0.9839
0.0297	1.43	500	0.0223	0.4950	0.9900	0.9900	nan	0.9900	0.0	0.9900
0.0299	1.49	520	0.0227	0.4961	0.9923	0.9923	nan	0.9923	0.0	0.9923
0.0213	1.55	540	0.0209	0.4947	0.9895	0.9895	nan	0.9895	0.0	0.9895
0.0269	1.6	560	0.0214	0.4909	0.9817	0.9817	nan	0.9817	0.0	0.9817
0.2199	1.66	580	0.0216	0.4956	0.9912	0.9912	nan	0.9912	0.0	0.9912
0.0191	1.72	600	0.0208	0.4935	0.9869	0.9869	nan	0.9869	0.0	0.9869
0.0265	1.78	620	0.0201	0.4941	0.9882	0.9882	nan	0.9882	0.0	0.9882
0.0244	1.83	640	0.0213	0.4910	0.9820	0.9820	nan	0.9820	0.0	0.9820
0.0172	1.89	660	0.0199	0.4929	0.9858	0.9858	nan	0.9858	0.0	0.9858
0.0339	1.95	680	0.0190	0.4930	0.9859	0.9859	nan	0.9859	0.0	0.9859
0.027	2.01	700	0.0192	0.4953	0.9906	0.9906	nan	0.9906	0.0	0.9906
0.0221	2.06	720	0.0195	0.4915	0.9830	0.9830	nan	0.9830	0.0	0.9830
0.0461	2.12	740	0.0188	0.4953	0.9905	0.9905	nan	0.9905	0.0	0.9905
0.0444	2.18	760	0.0189	0.4957	0.9914	0.9914	nan	0.9914	0.0	0.9914
0.0211	2.23	780	0.0184	0.4949	0.9898	0.9898	nan	0.9898	0.0	0.9898
0.0221	2.29	800	0.0186	0.4963	0.9925	0.9925	nan	0.9925	0.0	0.9925
0.0165	2.35	820	0.0181	0.4942	0.9883	0.9883	nan	0.9883	0.0	0.9883
0.0171	2.41	840	0.0181	0.4923	0.9846	0.9846	nan	0.9846	0.0	0.9846
0.0202	2.46	860	0.0178	0.4958	0.9915	0.9915	nan	0.9915	0.0	0.9915
0.0222	2.52	880	0.0178	0.4922	0.9844	0.9844	nan	0.9844	0.0	0.9844
0.018	2.58	900	0.0162	0.4949	0.9898	0.9898	nan	0.9898	0.0	0.9898
0.0288	2.64	920	0.0168	0.4943	0.9887	0.9887	nan	0.9887	0.0	0.9887
0.016	2.69	940	0.0178	0.4968	0.9936	0.9936	nan	0.9936	0.0	0.9936
0.0184	2.75	960	0.0172	0.4935	0.9870	0.9870	nan	0.9870	0.0	0.9870
0.0172	2.81	980	0.0175	0.4950	0.9900	0.9900	nan	0.9900	0.0	0.9900
0.0168	2.87	1000	0.0172	0.4951	0.9902	0.9902	nan	0.9902	0.0	0.9902
0.0197	2.92	1020	0.0169	0.4961	0.9923	0.9923	nan	0.9923	0.0	0.9923
0.0177	2.98	1040	0.0170	0.4961	0.9922	0.9922	nan	0.9922	0.0	0.9922
0.0377	3.04	1060	0.0163	0.4944	0.9888	0.9888	nan	0.9888	0.0	0.9888
0.0168	3.09	1080	0.0162	0.4953	0.9906	0.9906	nan	0.9906	0.0	0.9906
0.0167	3.15	1100	0.0166	0.4961	0.9922	0.9922	nan	0.9922	0.0	0.9922
0.0213	3.21	1120	0.0164	0.4948	0.9895	0.9895	nan	0.9895	0.0	0.9895
0.0195	3.27	1140	0.0162	0.4947	0.9894	0.9894	nan	0.9894	0.0	0.9894
0.014	3.32	1160	0.0160	0.4950	0.9900	0.9900	nan	0.9900	0.0	0.9900
0.0221	3.38	1180	0.0164	0.4961	0.9922	0.9922	nan	0.9922	0.0	0.9922
0.0162	3.44	1200	0.0159	0.4945	0.9890	0.9890	nan	0.9890	0.0	0.9890
0.0153	3.5	1220	0.0152	0.4957	0.9914	0.9914	nan	0.9914	0.0	0.9914
0.0145	3.55	1240	0.0161	0.4935	0.9871	0.9871	nan	0.9871	0.0	0.9871
0.0139	3.61	1260	0.0155	0.4951	0.9902	0.9902	nan	0.9902	0.0	0.9902
0.0153	3.67	1280	0.0157	0.4942	0.9884	0.9884	nan	0.9884	0.0	0.9884
0.0156	3.72	1300	0.0157	0.4949	0.9898	0.9898	nan	0.9898	0.0	0.9898
0.033	3.78	1320	0.0157	0.4952	0.9903	0.9903	nan	0.9903	0.0	0.9903
0.0219	3.84	1340	0.0153	0.4957	0.9915	0.9915	nan	0.9915	0.0	0.9915
0.0166	3.9	1360	0.0162	0.4935	0.9871	0.9871	nan	0.9871	0.0	0.9871
0.0168	3.95	1380	0.0157	0.4949	0.9897	0.9897	nan	0.9897	0.0	0.9897
0.0177	4.01	1400	0.0153	0.4966	0.9932	0.9932	nan	0.9932	0.0	0.9932
0.0136	4.07	1420	0.0150	0.4952	0.9905	0.9905	nan	0.9905	0.0	0.9905
0.0334	4.13	1440	0.0156	0.4956	0.9912	0.9912	nan	0.9912	0.0	0.9912
0.019	4.18	1460	0.0154	0.4950	0.9899	0.9899	nan	0.9899	0.0	0.9899
0.0147	4.24	1480	0.0148	0.4960	0.9920	0.9920	nan	0.9920	0.0	0.9920
0.0135	4.3	1500	0.0146	0.4951	0.9902	0.9902	nan	0.9902	0.0	0.9902
0.0186	4.36	1520	0.0143	0.4966	0.9933	0.9933	nan	0.9933	0.0	0.9933
0.0153	4.41	1540	0.0141	0.4954	0.9909	0.9909	nan	0.9909	0.0	0.9909
0.0181	4.47	1560	0.0145	0.4954	0.9908	0.9908	nan	0.9908	0.0	0.9908
0.0266	4.53	1580	0.0146	0.4953	0.9907	0.9907	nan	0.9907	0.0	0.9907
0.0141	4.58	1600	0.0147	0.4952	0.9904	0.9904	nan	0.9904	0.0	0.9904
0.0145	4.64	1620	0.0150	0.4947	0.9894	0.9894	nan	0.9894	0.0	0.9894
0.0128	4.7	1640	0.0151	0.4964	0.9928	0.9928	nan	0.9928	0.0	0.9928
0.0119	4.76	1660	0.0143	0.4948	0.9897	0.9897	nan	0.9897	0.0	0.9897
0.0133	4.81	1680	0.0144	0.4950	0.9900	0.9900	nan	0.9900	0.0	0.9900
0.0151	4.87	1700	0.0143	0.4956	0.9911	0.9911	nan	0.9911	0.0	0.9911
0.0211	4.93	1720	0.0149	0.4965	0.9930	0.9930	nan	0.9930	0.0	0.9930
0.0136	4.99	1740	0.0144	0.4964	0.9928	0.9928	nan	0.9928	0.0	0.9928
0.0129	5.04	1760	0.0142	0.4967	0.9934	0.9934	nan	0.9934	0.0	0.9934
0.0176	5.1	1780	0.0142	0.4965	0.9930	0.9930	nan	0.9930	0.0	0.9930
0.0119	5.16	1800	0.0141	0.4958	0.9916	0.9916	nan	0.9916	0.0	0.9916
0.021	5.21	1820	0.0143	0.4960	0.9920	0.9920	nan	0.9920	0.0	0.9920
0.0146	5.27	1840	0.0137	0.4961	0.9922	0.9922	nan	0.9922	0.0	0.9922
0.0158	5.33	1860	0.0138	0.4953	0.9905	0.9905	nan	0.9905	0.0	0.9905
0.014	5.39	1880	0.0142	0.4956	0.9913	0.9913	nan	0.9913	0.0	0.9913
0.0145	5.44	1900	0.0145	0.4952	0.9905	0.9905	nan	0.9905	0.0	0.9905
0.019	5.5	1920	0.0145	0.4960	0.9920	0.9920	nan	0.9920	0.0	0.9920
0.0134	5.56	1940	0.0143	0.4958	0.9915	0.9915	nan	0.9915	0.0	0.9915
0.011	5.62	1960	0.0141	0.4955	0.9910	0.9910	nan	0.9910	0.0	0.9910
0.0159	5.67	1980	0.0143	0.4971	0.9942	0.9942	nan	0.9942	0.0	0.9942
0.0132	5.73	2000	0.0140	0.4966	0.9933	0.9933	nan	0.9933	0.0	0.9933
0.017	5.79	2020	0.0136	0.4964	0.9928	0.9928	nan	0.9928	0.0	0.9928
0.0156	5.85	2040	0.0139	0.4951	0.9902	0.9902	nan	0.9902	0.0	0.9902
0.0169	5.9	2060	0.0142	0.4943	0.9887	0.9887	nan	0.9887	0.0	0.9887
0.0337	5.96	2080	0.0145	0.4967	0.9933	0.9933	nan	0.9933	0.0	0.9933
0.0158	6.02	2100	0.0141	0.4949	0.9898	0.9898	nan	0.9898	0.0	0.9898
0.0401	6.07	2120	0.0139	0.4956	0.9912	0.9912	nan	0.9912	0.0	0.9912
0.0629	6.13	2140	0.0138	0.4952	0.9904	0.9904	nan	0.9904	0.0	0.9904
0.0143	6.19	2160	0.0142	0.4967	0.9935	0.9935	nan	0.9935	0.0	0.9935
0.0133	6.25	2180	0.0135	0.4957	0.9915	0.9915	nan	0.9915	0.0	0.9915
0.0326	6.3	2200	0.0139	0.4963	0.9925	0.9925	nan	0.9925	0.0	0.9925
0.0141	6.36	2220	0.0133	0.4955	0.9910	0.9910	nan	0.9910	0.0	0.9910
0.0119	6.42	2240	0.0134	0.4958	0.9915	0.9915	nan	0.9915	0.0	0.9915
0.0133	6.48	2260	0.0139	0.4962	0.9924	0.9924	nan	0.9924	0.0	0.9924
0.0123	6.53	2280	0.0138	0.4967	0.9934	0.9934	nan	0.9934	0.0	0.9934
0.014	6.59	2300	0.0138	0.4962	0.9925	0.9925	nan	0.9925	0.0	0.9925
0.0137	6.65	2320	0.0136	0.4958	0.9916	0.9916	nan	0.9916	0.0	0.9916
0.0173	6.7	2340	0.0138	0.4964	0.9928	0.9928	nan	0.9928	0.0	0.9928
0.0137	6.76	2360	0.0136	0.4953	0.9905	0.9905	nan	0.9905	0.0	0.9905
0.0153	6.82	2380	0.0134	0.4958	0.9916	0.9916	nan	0.9916	0.0	0.9916
0.0135	6.88	2400	0.0137	0.4963	0.9926	0.9926	nan	0.9926	0.0	0.9926
0.0151	6.93	2420	0.0137	0.4952	0.9904	0.9904	nan	0.9904	0.0	0.9904
0.0122	6.99	2440	0.0134	0.4959	0.9918	0.9918	nan	0.9918	0.0	0.9918
0.013	7.05	2460	0.0135	0.4970	0.9941	0.9941	nan	0.9941	0.0	0.9941
0.0134	7.11	2480	0.0133	0.4964	0.9928	0.9928	nan	0.9928	0.0	0.9928
0.0145	7.16	2500	0.0134	0.4962	0.9924	0.9924	nan	0.9924	0.0	0.9924
0.028	7.22	2520	0.0135	0.4962	0.9924	0.9924	nan	0.9924	0.0	0.9924
0.0288	7.28	2540	0.0137	0.4967	0.9933	0.9933	nan	0.9933	0.0	0.9933
0.0117	7.34	2560	0.0135	0.4964	0.9927	0.9927	nan	0.9927	0.0	0.9927
0.013	7.39	2580	0.0136	0.4966	0.9932	0.9932	nan	0.9932	0.0	0.9932
0.0158	7.45	2600	0.0134	0.4950	0.9899	0.9899	nan	0.9899	0.0	0.9899
0.0135	7.51	2620	0.0134	0.4964	0.9928	0.9928	nan	0.9928	0.0	0.9928
0.0136	7.56	2640	0.0140	0.4967	0.9935	0.9935	nan	0.9935	0.0	0.9935
0.0396	7.62	2660	0.0133	0.4961	0.9922	0.9922	nan	0.9922	0.0	0.9922
0.0109	7.68	2680	0.0134	0.4963	0.9925	0.9925	nan	0.9925	0.0	0.9925
0.0148	7.74	2700	0.0133	0.4963	0.9925	0.9925	nan	0.9925	0.0	0.9925
0.0121	7.79	2720	0.0140	0.4945	0.9890	0.9890	nan	0.9890	0.0	0.9890
0.0109	7.85	2740	0.0139	0.4957	0.9913	0.9913	nan	0.9913	0.0	0.9913
0.014	7.91	2760	0.0135	0.4957	0.9915	0.9915	nan	0.9915	0.0	0.9915
0.0199	7.97	2780	0.0134	0.4959	0.9917	0.9917	nan	0.9917	0.0	0.9917
0.0119	8.02	2800	0.0136	0.4958	0.9916	0.9916	nan	0.9916	0.0	0.9916
0.0129	8.08	2820	0.0136	0.4962	0.9924	0.9924	nan	0.9924	0.0	0.9924
0.0108	8.14	2840	0.0134	0.4959	0.9917	0.9917	nan	0.9917	0.0	0.9917
0.0209	8.19	2860	0.0136	0.4960	0.9920	0.9920	nan	0.9920	0.0	0.9920
0.0154	8.25	2880	0.0137	0.4964	0.9928	0.9928	nan	0.9928	0.0	0.9928
0.0141	8.31	2900	0.0132	0.4965	0.9929	0.9929	nan	0.9929	0.0	0.9929
0.0187	8.37	2920	0.0131	0.4956	0.9912	0.9912	nan	0.9912	0.0	0.9912
0.0124	8.42	2940	0.0133	0.4959	0.9918	0.9918	nan	0.9918	0.0	0.9918
0.0135	8.48	2960	0.0132	0.4963	0.9926	0.9926	nan	0.9926	0.0	0.9926
0.0283	8.54	2980	0.0131	0.4958	0.9917	0.9917	nan	0.9917	0.0	0.9917
0.0691	8.6	3000	0.0131	0.4965	0.9930	0.9930	nan	0.9930	0.0	0.9930
0.0142	8.65	3020	0.0131	0.4965	0.9929	0.9929	nan	0.9929	0.0	0.9929
0.0155	8.71	3040	0.0130	0.4966	0.9931	0.9931	nan	0.9931	0.0	0.9931
0.0115	8.77	3060	0.0129	0.4966	0.9932	0.9932	nan	0.9932	0.0	0.9932
0.0095	8.83	3080	0.0130	0.4963	0.9927	0.9927	nan	0.9927	0.0	0.9927
0.012	8.88	3100	0.0132	0.4954	0.9907	0.9907	nan	0.9907	0.0	0.9907
0.0153	8.94	3120	0.0132	0.4965	0.9930	0.9930	nan	0.9930	0.0	0.9930
0.0141	9.0	3140	0.0134	0.4958	0.9917	0.9917	nan	0.9917	0.0	0.9917
0.0141	9.05	3160	0.0133	0.4958	0.9915	0.9915	nan	0.9915	0.0	0.9915
0.016	9.11	3180	0.0133	0.4964	0.9929	0.9929	nan	0.9929	0.0	0.9929
0.017	9.17	3200	0.0132	0.4965	0.9929	0.9929	nan	0.9929	0.0	0.9929
0.0245	9.23	3220	0.0132	0.4961	0.9921	0.9921	nan	0.9921	0.0	0.9921
0.0101	9.28	3240	0.0132	0.4962	0.9924	0.9924	nan	0.9924	0.0	0.9924
0.012	9.34	3260	0.0133	0.4959	0.9917	0.9917	nan	0.9917	0.0	0.9917
0.0111	9.4	3280	0.0133	0.4964	0.9928	0.9928	nan	0.9928	0.0	0.9928
0.0148	9.46	3300	0.0132	0.4962	0.9925	0.9925	nan	0.9925	0.0	0.9925
0.0124	9.51	3320	0.0135	0.4967	0.9934	0.9934	nan	0.9934	0.0	0.9934
0.0209	9.57	3340	0.0133	0.4963	0.9926	0.9926	nan	0.9926	0.0	0.9926
0.0134	9.63	3360	0.0132	0.4960	0.9920	0.9920	nan	0.9920	0.0	0.9920
0.0146	9.68	3380	0.0132	0.4958	0.9916	0.9916	nan	0.9916	0.0	0.9916
0.0217	9.74	3400	0.0132	0.4961	0.9923	0.9923	nan	0.9923	0.0	0.9923
0.0142	9.8	3420	0.0131	0.4961	0.9923	0.9923	nan	0.9923	0.0	0.9923
0.0134	9.86	3440	0.0131	0.4959	0.9918	0.9918	nan	0.9918	0.0	0.9918
0.0131	9.91	3460	0.0131	0.4960	0.9920	0.9920	nan	0.9920	0.0	0.9920
0.0136	9.97	3480	0.0131	0.4961	0.9922	0.9922	nan	0.9922	0.0	0.9922

Framework versions

• Transformers 4.35.0
• Pytorch 2.2.2
• Datasets 2.14.6
• Tokenizers 0.14.1