Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Circuit	Iou Circuit
0.4185	1.0	20	0.5878	0.3632	0.7265	0.7265	nan	0.7265	0.7265
0.4477	2.0	40	0.4288	0.4894	0.9788	0.9788	nan	0.9788	0.9788
0.9304	3.0	60	0.2053	0.4520	0.9041	0.9041	nan	0.9041	0.9041
0.1409	4.0	80	0.1817	0.4738	0.9477	0.9477	nan	0.9477	0.9477
0.392	5.0	100	0.1824	0.4900	0.9800	0.9800	nan	0.9800	0.9800
0.1589	6.0	120	0.1594	0.4814	0.9628	0.9628	nan	0.9628	0.9628
0.1848	7.0	140	0.1551	0.4625	0.9251	0.9251	nan	0.9251	0.9251
0.0874	8.0	160	0.1503	0.4829	0.9657	0.9657	nan	0.9657	0.9657
0.2172	9.0	180	0.1558	0.4591	0.9182	0.9182	nan	0.9182	0.9182
0.9914	10.0	200	0.1457	0.4698	0.9396	0.9396	nan	0.9396	0.9396
0.2387	11.0	220	0.1494	0.4709	0.9419	0.9419	nan	0.9419	0.9419
0.1242	12.0	240	0.1463	0.4743	0.9486	0.9486	nan	0.9486	0.9486
0.0819	13.0	260	0.1492	0.4757	0.9515	0.9515	nan	0.9515	0.9515
0.6077	14.0	280	0.1442	0.4793	0.9586	0.9586	nan	0.9586	0.9586
0.3156	15.0	300	0.1430	0.4813	0.9627	0.9627	nan	0.9627	0.9627
0.2564	16.0	320	0.1483	0.4673	0.9347	0.9347	nan	0.9347	0.9347
0.107	17.0	340	0.1467	0.4695	0.9390	0.9390	nan	0.9390	0.9390
1.1592	18.0	360	0.1437	0.4814	0.9628	0.9628	nan	0.9628	0.9628
0.0586	19.0	380	0.1396	0.4811	0.9622	0.9622	nan	0.9622	0.9622
0.9815	20.0	400	0.1399	0.4812	0.9624	0.9624	nan	0.9624	0.9624
0.3101	21.0	420	0.1411	0.4836	0.9672	0.9672	nan	0.9672	0.9672
0.2325	22.0	440	0.1395	0.4672	0.9344	0.9344	nan	0.9344	0.9344
0.1504	23.0	460	0.1420	0.4720	0.9441	0.9441	nan	0.9441	0.9441
0.2831	24.0	480	0.1393	0.4697	0.9395	0.9395	nan	0.9395	0.9395
0.0921	25.0	500	0.1418	0.4701	0.9401	0.9401	nan	0.9401	0.9401
0.141	26.0	520	0.1318	0.4648	0.9296	0.9296	nan	0.9296	0.9296
0.1381	27.0	540	0.1316	0.4697	0.9395	0.9395	nan	0.9395	0.9395
1.1864	28.0	560	0.1292	0.4774	0.9548	0.9548	nan	0.9548	0.9548
0.9492	29.0	580	0.1290	0.4709	0.9418	0.9418	nan	0.9418	0.9418
0.3061	30.0	600	0.1303	0.4536	0.9071	0.9071	nan	0.9071	0.9071
0.2511	31.0	620	0.1318	0.4725	0.9451	0.9451	nan	0.9451	0.9451
0.2706	32.0	640	0.1284	0.4790	0.9580	0.9580	nan	0.9580	0.9580
0.1508	33.0	660	0.1264	0.4698	0.9396	0.9396	nan	0.9396	0.9396
0.2802	34.0	680	0.1308	0.4733	0.9467	0.9467	nan	0.9467	0.9467
0.1897	35.0	700	0.1315	0.4681	0.9361	0.9361	nan	0.9361	0.9361
0.1981	36.0	720	0.1289	0.4766	0.9531	0.9531	nan	0.9531	0.9531
0.2742	37.0	740	0.1284	0.4818	0.9635	0.9635	nan	0.9635	0.9635
0.0418	38.0	760	0.1240	0.4762	0.9525	0.9525	nan	0.9525	0.9525
0.1946	39.0	780	0.1253	0.4750	0.9500	0.9500	nan	0.9500	0.9500
0.1692	40.0	800	0.1253	0.4836	0.9672	0.9672	nan	0.9672	0.9672
0.3071	41.0	820	0.1227	0.4751	0.9503	0.9503	nan	0.9503	0.9503
0.2003	42.0	840	0.1250	0.4762	0.9524	0.9524	nan	0.9524	0.9524
0.2099	43.0	860	0.1235	0.4740	0.9480	0.9480	nan	0.9480	0.9480
0.1218	44.0	880	0.1222	0.4743	0.9486	0.9486	nan	0.9486	0.9486
0.1583	45.0	900	0.1226	0.4708	0.9415	0.9415	nan	0.9415	0.9415
0.1506	46.0	920	0.1215	0.4686	0.9372	0.9372	nan	0.9372	0.9372
0.0643	47.0	940	0.1234	0.4779	0.9559	0.9559	nan	0.9559	0.9559
0.2006	48.0	960	0.1213	0.4757	0.9515	0.9515	nan	0.9515	0.9515
0.0783	49.0	980	0.1241	0.4726	0.9452	0.9452	nan	0.9452	0.9452
0.0552	50.0	1000	0.1227	0.4744	0.9489	0.9489	nan	0.9489	0.9489

Framework versions

Transformers 4.36.2
Pytorch 1.11.0+cu115
Datasets 2.15.0
Tokenizers 0.15.0

yijisuk/segformer-b1-finetuned-segments-ic-chip-sample

segformer-b1-finetuned-segments-ic-chip-sample

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

yijisuk/
segformer-b1-finetuned-segments-ic-chip-sample