Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Circuit	Iou Circuit
0.0885	1.0	20	0.0858	0.4907	0.9815	0.9815	nan	0.9815	0.9815
0.1764	2.0	40	0.0854	0.4734	0.9469	0.9469	nan	0.9469	0.9469
0.0569	3.0	60	0.0854	0.4702	0.9404	0.9404	nan	0.9404	0.9404
0.0959	4.0	80	0.0851	0.4893	0.9786	0.9786	nan	0.9786	0.9786
0.2969	5.0	100	0.0825	0.4863	0.9727	0.9727	nan	0.9727	0.9727
0.1979	6.0	120	0.0824	0.4873	0.9746	0.9746	nan	0.9746	0.9746
0.0906	7.0	140	0.0824	0.4740	0.9480	0.9480	nan	0.9480	0.9480
0.2879	8.0	160	0.0821	0.4882	0.9764	0.9764	nan	0.9764	0.9764
0.1366	9.0	180	0.0807	0.4833	0.9666	0.9666	nan	0.9666	0.9666
0.1664	10.0	200	0.0813	0.4860	0.9720	0.9720	nan	0.9720	0.9720
0.1521	11.0	220	0.0831	0.4830	0.9660	0.9660	nan	0.9660	0.9660
0.2004	12.0	240	0.0795	0.4825	0.9651	0.9651	nan	0.9651	0.9651
0.1547	13.0	260	0.0793	0.4812	0.9625	0.9625	nan	0.9625	0.9625
0.4191	14.0	280	0.0788	0.4830	0.9659	0.9659	nan	0.9659	0.9659
0.0431	15.0	300	0.0782	0.4815	0.9630	0.9630	nan	0.9630	0.9630
1.3911	16.0	320	0.0793	0.4820	0.9640	0.9640	nan	0.9640	0.9640
0.0217	17.0	340	0.0814	0.4836	0.9671	0.9671	nan	0.9671	0.9671
0.1116	18.0	360	0.0789	0.4839	0.9678	0.9678	nan	0.9678	0.9678
0.3295	19.0	380	0.0791	0.4763	0.9526	0.9526	nan	0.9526	0.9526
0.0327	20.0	400	0.0792	0.4829	0.9658	0.9658	nan	0.9658	0.9658
0.2542	21.0	420	0.0787	0.4861	0.9722	0.9722	nan	0.9722	0.9722
0.1587	22.0	440	0.0783	0.4772	0.9543	0.9543	nan	0.9543	0.9543
0.2721	23.0	460	0.0804	0.4913	0.9825	0.9825	nan	0.9825	0.9825
0.0505	24.0	480	0.0781	0.4827	0.9655	0.9655	nan	0.9655	0.9655
0.1417	25.0	500	0.0801	0.4834	0.9669	0.9669	nan	0.9669	0.9669
0.1371	26.0	520	0.0777	0.4838	0.9676	0.9676	nan	0.9676	0.9676
0.1282	27.0	540	0.0773	0.4807	0.9613	0.9613	nan	0.9613	0.9613
0.057	28.0	560	0.0772	0.4829	0.9657	0.9657	nan	0.9657	0.9657
0.2592	29.0	580	0.0807	0.4872	0.9744	0.9744	nan	0.9744	0.9744
0.1687	30.0	600	0.0794	0.4825	0.9649	0.9649	nan	0.9649	0.9649
0.499	31.0	620	0.0805	0.4853	0.9706	0.9706	nan	0.9706	0.9706
0.1584	32.0	640	0.0790	0.4845	0.9691	0.9691	nan	0.9691	0.9691
0.0689	33.0	660	0.0785	0.4845	0.9690	0.9690	nan	0.9690	0.9690
1.3764	34.0	680	0.0790	0.4848	0.9696	0.9696	nan	0.9696	0.9696
0.2597	35.0	700	0.0808	0.4875	0.9751	0.9751	nan	0.9751	0.9751
1.0757	36.0	720	0.0761	0.4841	0.9681	0.9681	nan	0.9681	0.9681
0.6112	37.0	740	0.0779	0.4825	0.9650	0.9650	nan	0.9650	0.9650
0.2899	38.0	760	0.0787	0.4796	0.9591	0.9591	nan	0.9591	0.9591
0.3402	39.0	780	0.0777	0.4838	0.9676	0.9676	nan	0.9676	0.9676
0.0183	40.0	800	0.0771	0.4829	0.9657	0.9657	nan	0.9657	0.9657
0.1407	41.0	820	0.0774	0.4809	0.9617	0.9617	nan	0.9617	0.9617
0.4045	42.0	840	0.0767	0.4819	0.9638	0.9638	nan	0.9638	0.9638
0.2159	43.0	860	0.0780	0.4850	0.9699	0.9699	nan	0.9699	0.9699
0.0541	44.0	880	0.0768	0.4812	0.9624	0.9624	nan	0.9624	0.9624
0.0638	45.0	900	0.0774	0.4863	0.9726	0.9726	nan	0.9726	0.9726
0.0409	46.0	920	0.0788	0.4875	0.9749	0.9749	nan	0.9749	0.9749
0.1662	47.0	940	0.0774	0.4871	0.9743	0.9743	nan	0.9743	0.9743
0.1636	48.0	960	0.0783	0.4860	0.9720	0.9720	nan	0.9720	0.9720
0.033	49.0	980	0.0791	0.4882	0.9764	0.9764	nan	0.9764	0.9764
0.171	50.0	1000	0.0772	0.4863	0.9725	0.9725	nan	0.9725	0.9725

Framework versions

Transformers 4.36.2
Pytorch 1.11.0+cu115
Datasets 2.15.0
Tokenizers 0.15.0

yijisuk/segformer-b0-finetuned-segments-ic-chip-sample

segformer-b0-finetuned-segments-ic-chip-sample

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

yijisuk/
segformer-b0-finetuned-segments-ic-chip-sample