learning_rate: 6e-07
train_batch_size: 8
eval_batch_size: 8
seed: 1337
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: polynomial
training_steps: 20000

Training results

Training Loss	Epoch	Step	Accuracy No Text	Accuracy Text	Iou No Text	Iou Text	Validation Loss	Mean Accuracy	Mean Iou	Overall Accuracy
0.3719	1.0	125	0.9684	nan	0.9684	0.0	0.1986	0.9684	0.4842	0.9684
0.2348	2.0	250	0.9864	nan	0.9864	0.0	0.1336	0.9864	0.4932	0.9864
0.183	3.0	375	0.9747	nan	0.9747	0.0	0.1268	0.9747	0.4874	0.9747
0.1485	4.0	500	0.9802	nan	0.9802	0.0	0.1114	0.9802	0.4901	0.9802
0.1429	5.0	625	0.9757	nan	0.9757	0.0	0.1122	0.9757	0.4878	0.9757
0.1367	6.0	750	0.9834	nan	0.9834	0.0	0.1075	0.9834	0.4917	0.9834
0.1333	7.0	875	0.9793	nan	0.9793	0.0	0.1048	0.9793	0.4897	0.9793
0.1199	8.0	1000	0.9776	nan	0.9776	0.0	0.1009	0.9776	0.4888	0.9776
0.1201	9.0	1125	0.9806	nan	0.9806	0.0	0.1000	0.9806	0.4903	0.9806
0.1111	10.0	1250	0.9807	nan	0.9807	0.0	0.0998	0.9807	0.4904	0.9807
0.1128	11.0	1375	0.9792	nan	0.9792	0.0	0.0984	0.9792	0.4896	0.9792
0.1055	12.0	1500	0.9835	nan	0.9835	0.0	0.0941	0.9835	0.4918	0.9835
0.0988	13.0	1625	0.9815	nan	0.9815	0.0	0.0972	0.9815	0.4907	0.9815
0.0983	14.0	1750	0.9843	nan	0.9843	0.0	0.0947	0.9843	0.4921	0.9843
0.1045	15.0	1875	0.9794	nan	0.9794	0.0	0.0960	0.9794	0.4897	0.9794
0.1002	16.0	2000	0.9790	nan	0.9790	0.0	0.0976	0.9790	0.4895	0.9790
0.1072	17.0	2125	0.9776	nan	0.9776	0.0	0.1006	0.9776	0.4888	0.9776
0.1046	18.0	2250	0.9800	nan	0.9800	0.0	0.0938	0.9800	0.4900	0.9800
0.1072	19.0	2375	0.9800	nan	0.9800	0.0	0.0962	0.9800	0.4900	0.9800
0.1127	20.0	2500	0.9840	nan	0.9840	0.0	0.0918	0.9840	0.4920	0.9840
0.1017	21.0	2625	0.9782	nan	0.9782	0.0	0.0940	0.9782	0.4891	0.9782
0.0961	22.0	2750	0.9784	nan	0.9784	0.0	0.0964	0.9784	0.4892	0.9784
0.0951	23.0	2875	0.9821	nan	0.9821	0.0	0.0940	0.9821	0.4910	0.9821
0.0938	24.0	3000	0.9836	nan	0.9836	0.0	0.1005	0.9836	0.4918	0.9836
0.0949	25.0	3125	0.9803	nan	0.9803	0.0	0.1003	0.9803	0.4901	0.9803
0.0949	26.0	3250	0.9815	nan	0.9815	0.0	0.1015	0.9815	0.4908	0.9815
0.0949	27.0	3375	0.9780	nan	0.9780	0.0	0.0970	0.9780	0.4890	0.9780
0.0883	28.0	3500	0.9779	nan	0.9779	0.0	0.0967	0.9779	0.4890	0.9779
0.0846	29.0	3625	0.9849	nan	0.9849	0.0	0.0973	0.9849	0.4924	0.9849
0.0842	30.0	3750	0.9820	nan	0.9820	0.0	0.0946	0.9820	0.4910	0.9820
0.0814	31.0	3875	0.9819	nan	0.9819	0.0	0.0936	0.9819	0.4909	0.9819
0.0813	32.0	4000	0.9813	nan	0.9813	0.0	0.0938	0.9813	0.4906	0.9813
0.0817	33.0	4125	0.9812	nan	0.9812	0.0	0.0946	0.9812	0.4906	0.9812
0.0836	34.0	4250	0.9775	nan	0.9775	0.0	0.0940	0.9775	0.4888	0.9775
0.0836	35.0	4375	0.9811	nan	0.9811	0.0	0.0915	0.9811	0.4906	0.9811
0.0785	36.0	4500	0.9816	nan	0.9816	0.0	0.0951	0.9816	0.4908	0.9816
0.0746	37.0	4625	0.9757	nan	0.9757	0.0	0.0951	0.9757	0.4879	0.9757
0.0819	38.0	4750	0.9800	nan	0.9800	0.0	0.0952	0.9800	0.4900	0.9800
0.0731	39.0	4875	0.9797	nan	0.9797	0.0	0.0922	0.9797	0.4899	0.9797
0.0745	40.0	5000	0.9798	nan	0.9798	0.0	0.0939	0.9798	0.4899	0.9798
0.0755	41.0	5125	0.9802	nan	0.9802	0.0	0.0946	0.9802	0.4901	0.9802
0.0692	42.0	5250	0.9757	nan	0.9757	0.0	0.0976	0.9757	0.4879	0.9757
0.0798	43.0	5375	0.9804	nan	0.9804	0.0	0.0988	0.9804	0.4902	0.9804
0.076	44.0	5500	0.9798	nan	0.9798	0.0	0.0965	0.9798	0.4899	0.9798
0.0757	45.0	5625	0.9823	nan	0.9823	0.0	0.0914	0.9823	0.4912	0.9823
0.0702	46.0	5750	0.9781	nan	0.9781	0.0	0.0935	0.9781	0.4890	0.9781
0.0765	47.0	5875	0.9809	nan	0.9809	0.0	0.0966	0.9809	0.4905	0.9809
0.0724	48.0	6000	0.9833	nan	0.9833	0.0	0.0937	0.9833	0.4916	0.9833
0.0713	49.0	6125	0.9762	nan	0.9762	0.0	0.1017	0.9762	0.4881	0.9762
0.0677	50.0	6250	0.9804	nan	0.9804	0.0	0.0932	0.9804	0.4902	0.9804
0.0715	51.0	6375	0.9781	nan	0.9781	0.0	0.0975	0.9781	0.4891	0.9781
0.0713	52.0	6500	0.9833	nan	0.9833	0.0	0.0945	0.9833	0.4917	0.9833
0.0695	53.0	6625	0.9819	nan	0.9819	0.0	0.0951	0.9819	0.4910	0.9819
0.0648	54.0	6750	0.9825	nan	0.9825	0.0	0.0965	0.9825	0.4912	0.9825
0.0694	55.0	6875	0.9809	nan	0.9809	0.0	0.0946	0.9809	0.4905	0.9809
0.0665	56.0	7000	0.9824	nan	0.9824	0.0	0.1007	0.9824	0.4912	0.9824
0.0635	57.0	7125	0.9831	nan	0.9831	0.0	0.0971	0.9831	0.4916	0.9831
0.0628	58.0	7250	0.9785	nan	0.9785	0.0	0.1002	0.9785	0.4893	0.9785
0.0668	59.0	7375	0.9813	nan	0.9813	0.0	0.0960	0.9813	0.4906	0.9813
0.0648	60.0	7500	0.9796	nan	0.9796	0.0	0.0939	0.9796	0.4898	0.9796
0.064	61.0	7625	0.9786	nan	0.9786	0.0	0.0947	0.9786	0.4893	0.9786
0.0636	62.0	7750	0.9788	nan	0.9788	0.0	0.0985	0.9788	0.4894	0.9788
0.0653	63.0	7875	0.9812	nan	0.9812	0.0	0.0914	0.9812	0.4906	0.9812
0.0594	64.0	8000	0.9782	nan	0.9782	0.0	0.0966	0.9782	0.4891	0.9782
0.0608	65.0	8125	0.9794	nan	0.9794	0.0	0.0961	0.9794	0.4897	0.9794
0.0625	66.0	8250	0.9814	nan	0.9814	0.0	0.0954	0.9814	0.4907	0.9814
0.0646	67.0	8375	0.9801	nan	0.9801	0.0	0.0981	0.9801	0.4900	0.9801
0.0634	68.0	8500	0.9823	nan	0.9823	0.0	0.0996	0.9823	0.4911	0.9823
0.0611	69.0	8625	0.9810	nan	0.9810	0.0	0.1007	0.9810	0.4905	0.9810
0.0599	70.0	8750	0.9793	nan	0.9793	0.0	0.0929	0.9793	0.4896	0.9793
0.0583	71.0	8875	0.9825	nan	0.9825	0.0	0.0988	0.9825	0.4913	0.9825
0.0596	72.0	9000	0.9790	nan	0.9790	0.0	0.0955	0.9790	0.4895	0.9790
0.0598	73.0	9125	0.9800	nan	0.9800	0.0	0.1025	0.9800	0.4900	0.9800
0.0623	74.0	9250	0.9836	nan	0.9836	0.0	0.0997	0.9836	0.4918	0.9836
0.0637	75.0	9375	0.9782	nan	0.9782	0.0	0.0971	0.9782	0.4891	0.9782
0.0627	76.0	9500	0.9806	nan	0.9806	0.0	0.0934	0.9806	0.4903	0.9806
0.0566	77.0	9625	0.9830	nan	0.9830	0.0	0.1016	0.9830	0.4915	0.9830
0.0585	78.0	9750	0.9817	nan	0.9817	0.0	0.0915	0.9817	0.4908	0.9817
0.0574	79.0	9875	0.9814	nan	0.9814	0.0	0.0939	0.9814	0.4907	0.9814
0.0579	80.0	10000	0.9797	nan	0.9797	0.0	0.0996	0.9797	0.4899	0.9797
0.0564	81.0	10125	0.9801	nan	0.9801	0.0	0.0988	0.9801	0.4901	0.9801
0.0614	82.0	10250	0.9836	nan	0.9836	0.0	0.1011	0.9836	0.4918	0.9836
0.0556	83.0	10375	0.9817	nan	0.9817	0.0	0.0984	0.9817	0.4908	0.9817
0.0582	84.0	10500	0.9811	nan	0.9811	0.0	0.0964	0.9811	0.4906	0.9811
0.057	85.0	10625	0.9821	nan	0.9821	0.0	0.0956	0.9821	0.4911	0.9821
0.0552	86.0	10750	0.9804	nan	0.9804	0.0	0.1000	0.9804	0.4902	0.9804
0.059	87.0	10875	0.9828	nan	0.9828	0.0	0.0990	0.9828	0.4914	0.9828
0.0547	88.0	11000	0.9811	nan	0.9811	0.0	0.0959	0.9811	0.4905	0.9811
0.0532	89.0	11125	0.9819	nan	0.9819	0.0	0.0980	0.9819	0.4909	0.9819
0.0578	90.0	11250	0.9829	nan	0.9829	0.0	0.0954	0.9829	0.4915	0.9829
0.0552	91.0	11375	0.9817	nan	0.9817	0.0	0.1013	0.9817	0.4909	0.9817
0.0584	92.0	11500	0.9802	nan	0.9802	0.0	0.0986	0.9802	0.4901	0.9802
0.0528	93.0	11625	0.9806	nan	0.9806	0.0	0.1009	0.9806	0.4903	0.9806
0.0566	94.0	11750	0.9802	nan	0.9802	0.0	0.0983	0.9802	0.4901	0.9802
0.0541	95.0	11875	0.9806	nan	0.9806	0.0	0.1032	0.9806	0.4903	0.9806
0.0577	96.0	12000	0.9800	nan	0.9800	0.0	0.1030	0.9800	0.4900	0.9800
0.0567	97.0	12125	0.9796	nan	0.9796	0.0	0.1039	0.9796	0.4898	0.9796
0.056	98.0	12250	0.9789	nan	0.9789	0.0	0.1020	0.9789	0.4894	0.9789
0.0517	99.0	12375	0.9819	nan	0.9819	0.0	0.1004	0.9819	0.4910	0.9819
0.051	100.0	12500	0.9826	nan	0.9826	0.0	0.0990	0.9826	0.4913	0.9826
0.0523	101.0	12625	0.9826	nan	0.9826	0.0	0.0984	0.9826	0.4913	0.9826
0.0521	102.0	12750	0.9799	nan	0.9799	0.0	0.0987	0.9799	0.4900	0.9799
0.0518	103.0	12875	0.9819	nan	0.9819	0.0	0.1065	0.9819	0.4909	0.9819
0.0521	104.0	13000	0.9809	nan	0.9809	0.0	0.1052	0.9809	0.4904	0.9809
0.0556	105.0	13125	0.9818	nan	0.9818	0.0	0.1006	0.9818	0.4909	0.9818
0.0544	106.0	13250	0.9809	nan	0.9809	0.0	0.1045	0.9809	0.4904	0.9809
0.0549	107.0	13375	0.9823	nan	0.9823	0.0	0.1014	0.9823	0.4912	0.9823
0.054	108.0	13500	0.9809	nan	0.9809	0.0	0.1026	0.9809	0.4904	0.9809
0.0526	109.0	13625	0.9837	nan	0.9837	0.0	0.1052	0.9837	0.4918	0.9837
0.0524	110.0	13750	0.9830	nan	0.9830	0.0	0.0987	0.9830	0.4915	0.9830
0.0487	111.0	13875	0.1028	0.4900	0.9801	0.9801	nan	0.9801	0.0	0.9801
0.054	112.0	14000	0.1070	0.4915	0.9829	0.9829	nan	0.9829	0.0	0.9829
0.0531	113.0	14125	0.1046	0.4903	0.9806	0.9806	nan	0.9806	0.0	0.9806
0.0478	114.0	14250	0.1036	0.4915	0.9831	0.9831	nan	0.9831	0.0	0.9831
0.0511	115.0	14375	0.1040	0.4904	0.9807	0.9807	nan	0.9807	0.0	0.9807
0.05	116.0	14500	0.1038	0.4913	0.9826	0.9826	nan	0.9826	0.0	0.9826
0.0522	117.0	14625	0.1051	0.4907	0.9814	0.9814	nan	0.9814	0.0	0.9814
0.0492	118.0	14750	0.1012	0.4908	0.9817	0.9817	nan	0.9817	0.0	0.9817
0.0526	119.0	14875	0.1041	0.4905	0.9811	0.9811	nan	0.9811	0.0	0.9811
0.0483	120.0	15000	0.1048	0.4918	0.9836	0.9836	nan	0.9836	0.0	0.9836
0.0496	121.0	15125	0.1067	0.4904	0.9807	0.9807	nan	0.9807	0.0	0.9807
0.0486	122.0	15250	0.1090	0.4900	0.9799	0.9799	nan	0.9799	0.0	0.9799
0.0539	123.0	15375	0.1029	0.4898	0.9797	0.9797	nan	0.9797	0.0	0.9797
0.0507	124.0	15500	0.1043	0.4902	0.9804	0.9804	nan	0.9804	0.0	0.9804
0.0482	125.0	15625	0.1064	0.4896	0.9791	0.9791	nan	0.9791	0.0	0.9791
0.0487	126.0	15750	0.1070	0.4907	0.9813	0.9813	nan	0.9813	0.0	0.9813
0.0492	127.0	15875	0.1101	0.4918	0.9836	0.9836	nan	0.9836	0.0	0.9836
0.0479	128.0	16000	0.1045	0.4900	0.9800	0.9800	nan	0.9800	0.0	0.9800
0.0514	129.0	16125	0.1043	0.4910	0.9820	0.9820	nan	0.9820	0.0	0.9820
0.0505	130.0	16250	0.1070	0.4911	0.9821	0.9821	nan	0.9821	0.0	0.9821
0.0491	131.0	16375	0.1019	0.4905	0.9811	0.9811	nan	0.9811	0.0	0.9811
0.0477	132.0	16500	0.1009	0.4904	0.9808	0.9808	nan	0.9808	0.0	0.9808
0.0476	133.0	16625	0.1015	0.4909	0.9818	0.9818	nan	0.9818	0.0	0.9818
0.0462	134.0	16750	0.1060	0.4902	0.9804	0.9804	nan	0.9804	0.0	0.9804
0.0485	135.0	16875	0.1018	0.4898	0.9795	0.9795	nan	0.9795	0.0	0.9795
0.0483	136.0	17000	0.1056	0.4898	0.9796	0.9796	nan	0.9796	0.0	0.9796
0.0503	137.0	17125	0.1044	0.4910	0.9820	0.9820	nan	0.9820	0.0	0.9820
0.0514	138.0	17250	0.1053	0.4906	0.9813	0.9813	nan	0.9813	0.0	0.9813
0.0446	139.0	17375	0.1051	0.4904	0.9808	0.9808	nan	0.9808	0.0	0.9808
0.047	140.0	17500	0.1071	0.4903	0.9807	0.9807	nan	0.9807	0.0	0.9807
0.0467	141.0	17625	0.1085	0.4914	0.9828	0.9828	nan	0.9828	0.0	0.9828
0.0476	142.0	17750	0.1077	0.4916	0.9832	0.9832	nan	0.9832	0.0	0.9832
0.0472	143.0	17875	0.1122	0.4909	0.9818	0.9818	nan	0.9818	0.0	0.9818
0.0477	144.0	18000	0.1043	0.4904	0.9808	0.9808	nan	0.9808	0.0	0.9808
0.0467	145.0	18125	0.1051	0.4898	0.9797	0.9797	nan	0.9797	0.0	0.9797
0.0493	146.0	18250	0.1049	0.4897	0.9795	0.9795	nan	0.9795	0.0	0.9795
0.0485	147.0	18375	0.1059	0.4905	0.9810	0.9810	nan	0.9810	0.0	0.9810
0.0462	148.0	18500	0.1057	0.4893	0.9787	0.9787	nan	0.9787	0.0	0.9787
0.0474	149.0	18625	0.1037	0.4900	0.9800	0.9800	nan	0.9800	0.0	0.9800
0.0506	150.0	18750	0.1052	0.4907	0.9814	0.9814	nan	0.9814	0.0	0.9814
0.0479	151.0	18875	0.1069	0.4903	0.9805	0.9805	nan	0.9805	0.0	0.9805
0.0439	152.0	19000	0.1080	0.4908	0.9816	0.9816	nan	0.9816	0.0	0.9816
0.0492	153.0	19125	0.1019	0.4904	0.9808	0.9808	nan	0.9808	0.0	0.9808
0.0442	154.0	19250	0.1053	0.4910	0.9821	0.9821	nan	0.9821	0.0	0.9821
0.0484	155.0	19375	0.1032	0.4909	0.9819	0.9819	nan	0.9819	0.0	0.9819
0.0466	156.0	19500	0.1039	0.4906	0.9812	0.9812	nan	0.9812	0.0	0.9812
0.0444	157.0	19625	0.1038	0.4904	0.9809	0.9809	nan	0.9809	0.0	0.9809
0.0463	158.0	19750	0.1038	0.4907	0.9814	0.9814	nan	0.9814	0.0	0.9814
0.0465	159.0	19875	0.1054	0.4907	0.9815	0.9815	nan	0.9815	0.0	0.9815
0.046	160.0	20000	0.1042	0.4902	0.9804	0.9804	nan	0.9804	0.0	0.9804

Framework versions

Transformers 4.49.0.dev0
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

Saumya-Mundra/segformer-finetuned-tt-1000-2k

segformer-finetuned-tt-1000-2k

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Saumya-Mundra/
segformer-finetuned-tt-1000-2k