learning_rate: 6e-05
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: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Text	Accuracy No Text	Iou No Text
No log	1.0	29	0.2604	0.4706	0.9411	0.9411	nan	0.9411	0.9411
No log	2.0	58	0.2094	0.4778	0.9557	0.9557	nan	0.9557	0.9557
No log	3.0	87	0.1818	0.4823	0.9647	0.9647	nan	0.9647	0.9647
0.323	4.0	116	0.2098	0.4696	0.9392	0.9392	nan	0.9392	0.9392
0.323	5.0	145	0.1717	0.4849	0.9699	0.9699	nan	0.9699	0.9699
0.323	6.0	174	0.1484	0.4840	0.9681	0.9681	nan	0.9681	0.9681
0.2027	7.0	203	0.1616	0.4815	0.9631	0.9631	nan	0.9631	0.9631
0.2027	8.0	232	0.1503	0.4864	0.9728	0.9728	nan	0.9728	0.9728
0.2027	9.0	261	0.1628	0.4783	0.9566	0.9566	nan	0.9566	0.9566
0.2027	10.0	290	0.1424	0.4835	0.9670	0.9670	nan	0.9670	0.9670
0.1693	11.0	319	0.1236	0.4903	0.9806	0.9806	nan	0.9806	0.9806
0.1693	12.0	348	0.1388	0.4816	0.9632	0.9632	nan	0.9632	0.9632
0.1693	13.0	377	0.1554	0.4788	0.9577	0.9577	nan	0.9577	0.9577
0.1502	14.0	406	0.1537	0.4761	0.9521	0.9521	nan	0.9521	0.9521
0.1502	15.0	435	0.1478	0.4781	0.9562	0.9562	nan	0.9562	0.9562
0.1502	16.0	464	0.1367	0.4825	0.9651	0.9651	nan	0.9651	0.9651
0.1502	17.0	493	0.1330	0.4851	0.9703	0.9703	nan	0.9703	0.9703
0.127	18.0	522	0.1322	0.4848	0.9696	0.9696	nan	0.9696	0.9696
0.127	19.0	551	0.1341	0.4842	0.9684	0.9684	nan	0.9684	0.9684
0.127	20.0	580	0.1244	0.4882	0.9764	0.9764	nan	0.9764	0.9764
0.1173	21.0	609	0.1200	0.4896	0.9793	0.9793	nan	0.9793	0.9793
0.1173	22.0	638	0.1374	0.4826	0.9653	0.9653	nan	0.9653	0.9653
0.1173	23.0	667	0.1248	0.4884	0.9768	0.9768	nan	0.9768	0.9768
0.1173	24.0	696	0.1262	0.4857	0.9714	0.9714	nan	0.9714	0.9714
0.1193	25.0	725	0.1235	0.4860	0.9720	0.9720	nan	0.9720	0.9720
0.1193	26.0	754	0.1323	0.4838	0.9677	0.9677	nan	0.9677	0.9677
0.1193	27.0	783	0.1235	0.4853	0.9707	0.9707	nan	0.9707	0.9707
0.0912	28.0	812	0.1349	0.4816	0.9632	0.9632	nan	0.9632	0.9632
0.0912	29.0	841	0.1408	0.4816	0.9632	0.9632	nan	0.9632	0.9632
0.0912	30.0	870	0.1206	0.4877	0.9754	0.9754	nan	0.9754	0.9754
0.0912	31.0	899	0.1347	0.4823	0.9646	0.9646	nan	0.9646	0.9646
0.1005	32.0	928	0.1188	0.4879	0.9758	0.9758	nan	0.9758	0.9758
0.1005	33.0	957	0.1234	0.4891	0.9781	0.9781	nan	0.9781	0.9781
0.1005	34.0	986	0.1206	0.4924	0.9849	0.9849	nan	0.9849	0.9849
0.0933	35.0	1015	0.1234	0.4895	0.9790	0.9790	nan	0.9790	0.9790
0.0933	36.0	1044	0.1192	0.4877	0.9753	0.9753	nan	0.9753	0.9753
0.0933	37.0	1073	0.1388	0.4807	0.9615	0.9615	nan	0.9615	0.9615
0.0937	38.0	1102	0.1255	0.4860	0.9719	0.9719	nan	0.9719	0.9719
0.0937	39.0	1131	0.1199	0.4877	0.9755	0.9755	nan	0.9755	0.9755
0.0937	40.0	1160	0.1194	0.4899	0.9799	0.9799	nan	0.9799	0.9799
0.0937	41.0	1189	0.1274	0.4844	0.9687	0.9687	nan	0.9687	0.9687
0.0923	42.0	1218	0.1302	0.4852	0.9703	0.9703	nan	0.9703	0.9703
0.0923	43.0	1247	0.1297	0.4854	0.9709	0.9709	nan	0.9709	0.9709
0.0923	44.0	1276	0.1299	0.4850	0.9701	0.9701	nan	0.9701	0.9701
0.0812	45.0	1305	0.1259	0.4867	0.9733	0.9733	nan	0.9733	0.9733
0.0812	46.0	1334	0.1252	0.4883	0.9766	0.9766	nan	0.9766	0.9766
0.0812	47.0	1363	0.1223	0.4881	0.9762	0.9762	nan	0.9762	0.9762
0.0812	48.0	1392	0.1227	0.4879	0.9757	0.9757	nan	0.9757	0.9757
0.0901	49.0	1421	0.1224	0.4880	0.9759	0.9759	nan	0.9759	0.9759
0.0901	50.0	1450	0.1350	0.4818	0.9636	0.9636	nan	0.9636	0.9636
0.0901	51.0	1479	0.1285	0.4859	0.9719	0.9719	nan	0.9719	0.9719
0.083	52.0	1508	0.1286	0.4848	0.9695	0.9695	nan	0.9695	0.9695
0.083	53.0	1537	0.1285	0.4850	0.9700	0.9700	nan	0.9700	0.9700
0.083	54.0	1566	0.1252	0.4888	0.9775	0.9775	nan	0.9775	0.9775
0.083	55.0	1595	0.1259	0.4867	0.9734	0.9734	nan	0.9734	0.9734
0.0822	56.0	1624	0.1373	0.4821	0.9642	0.9642	nan	0.9642	0.9642
0.0822	57.0	1653	0.1248	0.4860	0.9719	0.9719	nan	0.9719	0.9719
0.0822	58.0	1682	0.1228	0.4881	0.9761	0.9761	nan	0.9761	0.9761
0.0777	59.0	1711	0.1315	0.4838	0.9677	0.9677	nan	0.9677	0.9677
0.0777	60.0	1740	0.1254	0.4883	0.9766	0.9766	nan	0.9766	0.9766
0.0777	61.0	1769	0.1341	0.4841	0.9682	0.9682	nan	0.9682	0.9682
0.0777	62.0	1798	0.1269	0.4863	0.9726	0.9726	nan	0.9726	0.9726
0.079	63.0	1827	0.1266	0.4878	0.9755	0.9755	nan	0.9755	0.9755
0.079	64.0	1856	0.1304	0.4853	0.9706	0.9706	nan	0.9706	0.9706
0.079	65.0	1885	0.1253	0.4873	0.9747	0.9747	nan	0.9747	0.9747
0.0781	66.0	1914	0.1283	0.4866	0.9731	0.9731	nan	0.9731	0.9731
0.0781	67.0	1943	0.1290	0.4892	0.9784	0.9784	nan	0.9784	0.9784
0.0781	68.0	1972	0.1363	0.4835	0.9669	0.9669	nan	0.9669	0.9669
0.0826	68.9655	2000	0.1299	0.4851	0.9702	0.9702	nan	0.9702	0.9702

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-225-2k

segformer-finetuned-tt-225-2k

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Saumya-Mundra/
segformer-finetuned-tt-225-2k