This model is a fine-tuned version of nvidia/mit-b2 on the vigneshgs7/segformer_open_cv_RGB_L_0_1 dataset. It achieves the following results on the evaluation set:

Loss: 0.0222
Mean Iou: 0.4959
Mean Accuracy: 0.9919
Overall Accuracy: 0.9919
Accuracy Background: nan
Accuracy Object: 0.9919
Iou Background: 0.0
Iou Object: 0.9919

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 Object
0.4097	0.06	20	0.4634	0.4794	0.9589	0.9589	nan	0.9589	0.9589
0.4192	0.11	40	0.2595	0.4800	0.9601	0.9601	nan	0.9601	0.9601
0.4005	0.17	60	0.1546	0.4720	0.9441	0.9441	nan	0.9441	0.9441
0.1912	0.23	80	0.1395	0.4780	0.9560	0.9560	nan	0.9560	0.9560
0.1286	0.29	100	0.1182	0.4775	0.9551	0.9551	nan	0.9551	0.9551
0.1012	0.34	120	0.0902	0.4738	0.9477	0.9477	nan	0.9477	0.9477
0.0798	0.4	140	0.0777	0.4812	0.9624	0.9624	nan	0.9624	0.9624
0.0593	0.46	160	0.0716	0.4849	0.9697	0.9697	nan	0.9697	0.9697
0.107	0.52	180	0.0675	0.4900	0.9800	0.9800	nan	0.9800	0.9800
0.0521	0.57	200	0.0553	0.4811	0.9621	0.9621	nan	0.9621	0.9621
0.045	0.63	220	0.0527	0.4915	0.9829	0.9829	nan	0.9829	0.9829
0.0447	0.69	240	0.0481	0.4785	0.9571	0.9571	nan	0.9571	0.9571
0.0381	0.74	260	0.0405	0.4878	0.9755	0.9755	nan	0.9755	0.9755
0.0392	0.8	280	0.0409	0.4861	0.9723	0.9723	nan	0.9723	0.9723
0.0364	0.86	300	0.0377	0.4878	0.9755	0.9755	nan	0.9755	0.9755
0.0481	0.92	320	0.0383	0.4920	0.9840	0.9840	nan	0.9840	0.9840
0.0424	0.97	340	0.0355	0.4909	0.9818	0.9818	nan	0.9818	0.9818
0.0371	1.03	360	0.0358	0.4866	0.9732	0.9732	nan	0.9732	0.9732
0.0224	1.09	380	0.0355	0.4897	0.9794	0.9794	nan	0.9794	0.9794
0.0358	1.15	400	0.0359	0.4885	0.9769	0.9769	nan	0.9769	0.9769
0.0235	1.2	420	0.0340	0.4877	0.9753	0.9753	nan	0.9753	0.9753
0.1746	1.26	440	0.0335	0.4927	0.9854	0.9854	nan	0.9854	0.9854
0.0253	1.32	460	0.0321	0.4889	0.9778	0.9778	nan	0.9778	0.9778
0.0247	1.38	480	0.0299	0.4907	0.9814	0.9814	nan	0.9814	0.9814
0.0351	1.43	500	0.0303	0.4907	0.9813	0.9813	nan	0.9813	0.9813
0.0203	1.49	520	0.0300	0.4906	0.9812	0.9812	nan	0.9812	0.9812
0.0254	1.55	540	0.0327	0.4859	0.9718	0.9718	nan	0.9718	0.9718
0.0272	1.6	560	0.0293	0.4908	0.9816	0.9816	nan	0.9816	0.9816
0.0295	1.66	580	0.0284	0.4908	0.9816	0.9816	nan	0.9816	0.9816
0.025	1.72	600	0.0286	0.4890	0.9779	0.9779	nan	0.9779	0.9779
0.0225	1.78	620	0.0283	0.4899	0.9799	0.9799	nan	0.9799	0.9799
0.1922	1.83	640	0.0264	0.4917	0.9834	0.9834	nan	0.9834	0.9834
0.0349	1.89	660	0.0265	0.4935	0.9871	0.9871	nan	0.9871	0.9871
0.023	1.95	680	0.0281	0.4887	0.9774	0.9774	nan	0.9774	0.9774
0.024	2.01	700	0.0262	0.4936	0.9872	0.9872	nan	0.9872	0.9872
0.0278	2.06	720	0.0261	0.4923	0.9846	0.9846	nan	0.9846	0.9846
0.0276	2.12	740	0.0263	0.4923	0.9845	0.9845	nan	0.9845	0.9845
0.0208	2.18	760	0.0262	0.4903	0.9806	0.9806	nan	0.9806	0.9806
0.0206	2.23	780	0.0258	0.4896	0.9792	0.9792	nan	0.9792	0.9792
0.017	2.29	800	0.0265	0.4887	0.9775	0.9775	nan	0.9775	0.9775
0.1898	2.35	820	0.0260	0.4902	0.9803	0.9803	nan	0.9803	0.9803
0.0167	2.41	840	0.0256	0.4942	0.9883	0.9883	nan	0.9883	0.9883
0.0212	2.46	860	0.0263	0.4892	0.9784	0.9784	nan	0.9784	0.9784
0.0182	2.52	880	0.0252	0.4900	0.9800	0.9800	nan	0.9800	0.9800
0.0218	2.58	900	0.0241	0.4918	0.9836	0.9836	nan	0.9836	0.9836
0.0197	2.64	920	0.0249	0.4895	0.9791	0.9791	nan	0.9791	0.9791
0.0254	2.69	940	0.0241	0.4910	0.9819	0.9819	nan	0.9819	0.9819
0.0276	2.75	960	0.0249	0.4908	0.9816	0.9816	nan	0.9816	0.9816
0.0167	2.81	980	0.0241	0.4929	0.9858	0.9858	nan	0.9858	0.9858
0.0173	2.87	1000	0.0241	0.4903	0.9806	0.9806	nan	0.9806	0.9806
0.081	2.92	1020	0.0251	0.4892	0.9783	0.9783	nan	0.9783	0.9783
0.0273	2.98	1040	0.0230	0.4921	0.9842	0.9842	nan	0.9842	0.9842
0.0384	3.04	1060	0.0232	0.4941	0.9881	0.9881	nan	0.9881	0.9881
0.0229	3.09	1080	0.0235	0.4932	0.9863	0.9863	nan	0.9863	0.9863
0.0329	3.15	1100	0.0231	0.4941	0.9882	0.9882	nan	0.9882	0.9882
0.0149	3.21	1120	0.0232	0.4942	0.9883	0.9883	nan	0.9883	0.9883
0.0163	3.27	1140	0.0237	0.4906	0.9813	0.9813	nan	0.9813	0.9813
0.0144	3.32	1160	0.0237	0.4903	0.9807	0.9807	nan	0.9807	0.9807
0.0196	3.38	1180	0.0225	0.4926	0.9851	0.9851	nan	0.9851	0.9851
0.0194	3.44	1200	0.0224	0.4921	0.9841	0.9841	nan	0.9841	0.9841
0.0182	3.5	1220	0.0224	0.4916	0.9832	0.9832	nan	0.9832	0.9832
0.0178	3.55	1240	0.0230	0.4954	0.9909	0.9909	nan	0.9909	0.9909
0.0291	3.61	1260	0.0221	0.4920	0.9840	0.9840	nan	0.9840	0.9840
0.0167	3.67	1280	0.0219	0.4934	0.9868	0.9868	nan	0.9868	0.9868
0.0142	3.72	1300	0.0216	0.4943	0.9886	0.9886	nan	0.9886	0.9886
0.0183	3.78	1320	0.0217	0.4927	0.9855	0.9855	nan	0.9855	0.9855
0.0156	3.84	1340	0.0216	0.4946	0.9892	0.9892	nan	0.9892	0.9892
0.0438	3.9	1360	0.0215	0.4932	0.9863	0.9863	nan	0.9863	0.9863
0.0265	3.95	1380	0.0217	0.4952	0.9904	0.9904	nan	0.9904	0.9904
0.0481	4.01	1400	0.0231	0.4943	0.9885	0.9885	nan	0.9885	0.9885
0.0163	4.07	1420	0.0227	0.4948	0.9896	0.9896	nan	0.9896	0.9896
0.0399	4.13	1440	0.0210	0.4941	0.9881	0.9881	nan	0.9881	0.9881
0.0178	4.18	1460	0.0221	0.4947	0.9894	0.9894	nan	0.9894	0.9894
0.0159	4.24	1480	0.0220	0.4940	0.9880	0.9880	nan	0.9880	0.9880
0.0159	4.3	1500	0.0212	0.4952	0.9903	0.9903	nan	0.9903	0.9903
0.0241	4.36	1520	0.0214	0.4945	0.9890	0.9890	nan	0.9890	0.9890
0.0159	4.41	1540	0.0215	0.4941	0.9882	0.9882	nan	0.9882	0.9882
0.0202	4.47	1560	0.0233	0.4953	0.9907	0.9907	nan	0.9907	0.9907
0.037	4.53	1580	0.0225	0.4950	0.9900	0.9900	nan	0.9900	0.9900
0.0203	4.58	1600	0.0229	0.4944	0.9889	0.9889	nan	0.9889	0.9889
0.0244	4.64	1620	0.0210	0.4948	0.9896	0.9896	nan	0.9896	0.9896
0.0202	4.7	1640	0.0209	0.4954	0.9909	0.9909	nan	0.9909	0.9909
0.0137	4.76	1660	0.0211	0.4940	0.9879	0.9879	nan	0.9879	0.9879
0.0152	4.81	1680	0.0210	0.4934	0.9868	0.9868	nan	0.9868	0.9868
0.0159	4.87	1700	0.0206	0.4955	0.9910	0.9910	nan	0.9910	0.9910
0.0202	4.93	1720	0.0207	0.4930	0.9861	0.9861	nan	0.9861	0.9861
0.0453	4.99	1740	0.0211	0.4929	0.9859	0.9859	nan	0.9859	0.9859
0.0203	5.04	1760	0.0207	0.4952	0.9904	0.9904	nan	0.9904	0.9904
0.014	5.1	1780	0.0207	0.4957	0.9913	0.9913	nan	0.9913	0.9913
0.0458	5.16	1800	0.0217	0.4959	0.9918	0.9918	nan	0.9918	0.9918
0.012	5.21	1820	0.0218	0.4945	0.9889	0.9889	nan	0.9889	0.9889
0.0444	5.27	1840	0.0227	0.4949	0.9897	0.9897	nan	0.9897	0.9897
0.0791	5.33	1860	0.0226	0.4942	0.9884	0.9884	nan	0.9884	0.9884
0.0349	5.39	1880	0.0222	0.4932	0.9865	0.9865	nan	0.9865	0.9865
0.0175	5.44	1900	0.0225	0.4943	0.9885	0.9885	nan	0.9885	0.9885
0.0191	5.5	1920	0.0222	0.4939	0.9878	0.9878	nan	0.9878	0.9878
0.0219	5.56	1940	0.0217	0.4950	0.9900	0.9900	nan	0.9900	0.9900
0.0251	5.62	1960	0.0225	0.4947	0.9895	0.9895	nan	0.9895	0.9895
0.0317	5.67	1980	0.0232	0.4943	0.9887	0.9887	nan	0.9887	0.9887
0.0177	5.73	2000	0.0232	0.4946	0.9892	0.9892	nan	0.9892	0.9892
0.0172	5.79	2020	0.0205	0.4939	0.9879	0.9879	nan	0.9879	0.9879
0.028	5.85	2040	0.0224	0.4968	0.9936	0.9936	nan	0.9936	0.9936
0.0144	5.9	2060	0.0202	0.4939	0.9877	0.9877	nan	0.9877	0.9877
0.0143	5.96	2080	0.0203	0.4953	0.9906	0.9906	nan	0.9906	0.9906
0.0161	6.02	2100	0.0199	0.4945	0.9890	0.9890	nan	0.9890	0.9890
0.014	6.07	2120	0.0202	0.4953	0.9905	0.9905	nan	0.9905	0.9905
0.0299	6.13	2140	0.0203	0.4932	0.9863	0.9863	nan	0.9863	0.9863
0.0152	6.19	2160	0.0201	0.4954	0.9908	0.9908	nan	0.9908	0.9908
0.0159	6.25	2180	0.0200	0.4956	0.9913	0.9913	nan	0.9913	0.9913
0.0135	6.3	2200	0.0214	0.4960	0.9920	0.9920	nan	0.9920	0.9920
0.0122	6.36	2220	0.0211	0.4939	0.9879	0.9879	nan	0.9879	0.9879
0.0198	6.42	2240	0.0203	0.4955	0.9911	0.9911	nan	0.9911	0.9911
0.0205	6.48	2260	0.0207	0.4948	0.9897	0.9897	nan	0.9897	0.9897
0.0144	6.53	2280	0.0205	0.4947	0.9893	0.9893	nan	0.9893	0.9893
0.0138	6.59	2300	0.0207	0.4956	0.9912	0.9912	nan	0.9912	0.9912
0.0228	6.65	2320	0.0224	0.4953	0.9906	0.9906	nan	0.9906	0.9906
0.0126	6.7	2340	0.0206	0.4949	0.9899	0.9899	nan	0.9899	0.9899
0.0134	6.76	2360	0.0208	0.4950	0.9900	0.9900	nan	0.9900	0.9900
0.0105	6.82	2380	0.0229	0.4954	0.9909	0.9909	nan	0.9909	0.9909
0.0407	6.88	2400	0.0219	0.4952	0.9905	0.9905	nan	0.9905	0.9905
0.0148	6.93	2420	0.0212	0.4948	0.9897	0.9897	nan	0.9897	0.9897
0.011	6.99	2440	0.0216	0.4955	0.9909	0.9909	nan	0.9909	0.9909
0.0149	7.05	2460	0.0221	0.4948	0.9895	0.9895	nan	0.9895	0.9895
0.0312	7.11	2480	0.0243	0.4956	0.9912	0.9912	nan	0.9912	0.9912
0.0146	7.16	2500	0.0236	0.4963	0.9927	0.9927	nan	0.9927	0.9927
0.0132	7.22	2520	0.0221	0.4954	0.9908	0.9908	nan	0.9908	0.9908
0.0314	7.28	2540	0.0214	0.4939	0.9878	0.9878	nan	0.9878	0.9878
0.0177	7.34	2560	0.0221	0.4951	0.9903	0.9903	nan	0.9903	0.9903
0.0213	7.39	2580	0.0223	0.4956	0.9912	0.9912	nan	0.9912	0.9912
0.0135	7.45	2600	0.0212	0.4953	0.9906	0.9906	nan	0.9906	0.9906
0.0361	7.51	2620	0.0223	0.4962	0.9924	0.9924	nan	0.9924	0.9924
0.0457	7.56	2640	0.0221	0.4957	0.9914	0.9914	nan	0.9914	0.9914
0.0191	7.62	2660	0.0238	0.4960	0.9919	0.9919	nan	0.9919	0.9919
0.0141	7.68	2680	0.0222	0.4951	0.9902	0.9902	nan	0.9902	0.9902
0.012	7.74	2700	0.0232	0.4959	0.9918	0.9918	nan	0.9918	0.9918
0.0134	7.79	2720	0.0226	0.4952	0.9904	0.9904	nan	0.9904	0.9904
0.0174	7.85	2740	0.0226	0.4957	0.9913	0.9913	nan	0.9913	0.9913
0.0163	7.91	2760	0.0215	0.4948	0.9895	0.9895	nan	0.9895	0.9895
0.0159	7.97	2780	0.0213	0.4960	0.9920	0.9920	nan	0.9920	0.9920
0.0122	8.02	2800	0.0206	0.4950	0.9900	0.9900	nan	0.9900	0.9900
0.0272	8.08	2820	0.0207	0.4947	0.9893	0.9893	nan	0.9893	0.9893
0.0178	8.14	2840	0.0214	0.4953	0.9907	0.9907	nan	0.9907	0.9907
0.1188	8.19	2860	0.0211	0.4946	0.9892	0.9892	nan	0.9892	0.9892
0.0128	8.25	2880	0.0222	0.4962	0.9924	0.9924	nan	0.9924	0.9924
0.0171	8.31	2900	0.0222	0.4955	0.9909	0.9909	nan	0.9909	0.9909
0.0522	8.37	2920	0.0227	0.4959	0.9918	0.9918	nan	0.9918	0.9918
0.0142	8.42	2940	0.0237	0.4960	0.9920	0.9920	nan	0.9920	0.9920
0.0422	8.48	2960	0.0234	0.4950	0.9901	0.9901	nan	0.9901	0.9901
0.0362	8.54	2980	0.0226	0.4954	0.9908	0.9908	nan	0.9908	0.9908
0.0187	8.6	3000	0.0220	0.4952	0.9903	0.9903	nan	0.9903	0.9903
0.0154	8.65	3020	0.0216	0.4948	0.9896	0.9896	nan	0.9896	0.9896
0.0387	8.71	3040	0.0219	0.4956	0.9912	0.9912	nan	0.9912	0.9912
0.038	8.77	3060	0.0214	0.4948	0.9896	0.9896	nan	0.9896	0.9896
0.0145	8.83	3080	0.0213	0.4955	0.9910	0.9910	nan	0.9910	0.9910
0.0129	8.88	3100	0.0210	0.4953	0.9906	0.9906	nan	0.9906	0.9906
0.0129	8.94	3120	0.0213	0.4953	0.9907	0.9907	nan	0.9907	0.9907
0.0148	9.0	3140	0.0220	0.4958	0.9916	0.9916	nan	0.9916	0.9916
0.0133	9.05	3160	0.0210	0.4946	0.9891	0.9891	nan	0.9891	0.9891
0.0158	9.11	3180	0.0213	0.4954	0.9908	0.9908	nan	0.9908	0.9908
0.0155	9.17	3200	0.0217	0.4957	0.9914	0.9914	nan	0.9914	0.9914
0.0202	9.23	3220	0.0218	0.4955	0.9911	0.9911	nan	0.9911	0.9911
0.0128	9.28	3240	0.0211	0.4953	0.9905	0.9905	nan	0.9905	0.9905
0.0304	9.34	3260	0.0218	0.4959	0.9918	0.9918	nan	0.9918	0.9918
0.0354	9.4	3280	0.0214	0.4954	0.9908	0.9908	nan	0.9908	0.9908
0.0188	9.46	3300	0.0214	0.4952	0.9903	0.9903	nan	0.9903	0.9903
0.0117	9.51	3320	0.0223	0.4961	0.9921	0.9921	nan	0.9921	0.9921
0.0175	9.57	3340	0.0215	0.4954	0.9907	0.9907	nan	0.9907	0.9907
0.0304	9.63	3360	0.0217	0.4954	0.9909	0.9909	nan	0.9909	0.9909
0.0166	9.68	3380	0.0216	0.4955	0.9909	0.9909	nan	0.9909	0.9909
0.0899	9.74	3400	0.0221	0.4962	0.9923	0.9923	nan	0.9923	0.9923
0.0128	9.8	3420	0.0216	0.4955	0.9910	0.9910	nan	0.9910	0.9910
0.0149	9.86	3440	0.0217	0.4955	0.9910	0.9910	nan	0.9910	0.9910
0.0192	9.91	3460	0.0216	0.4953	0.9906	0.9906	nan	0.9906	0.9906
0.0454	9.97	3480	0.0222	0.4959	0.9919	0.9919	nan	0.9919	0.9919

Framework versions

Transformers 4.35.0
Pytorch 2.2.2
Datasets 2.14.6
Tokenizers 0.14.1

vigneshgs7/segformer-b2-p142-cvat-2

segformer-b2-p142-cvat-2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vigneshgs7/
segformer-b2-p142-cvat-2