Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Matrix	Accuracy Austenite	Accuracy Martensite/austenite	Accuracy Precipitate	Accuracy Defect	Iou Matrix	Iou Austenite	Iou Martensite/austenite	Iou Precipitate	Iou Defect
0.2546	1.0	343	0.3220	0.5965	0.6868	0.8757	0.8517	0.9218	0.7201	0.0	0.9404	0.7384	0.8585	0.5502	0.0	0.8353
0.336	2.0	686	0.3159	0.5992	0.6766	0.8807	0.8816	0.9295	0.6220	0.0	0.9497	0.7474	0.8627	0.5429	0.0	0.8428
0.2976	3.0	1029	0.3087	0.6057	0.6971	0.8807	0.8383	0.9325	0.7561	0.0000	0.9583	0.7412	0.8629	0.5833	0.0000	0.8411
0.2791	4.0	1372	0.2907	0.6175	0.6995	0.8886	0.8717	0.9290	0.7401	0.0016	0.9548	0.7608	0.8674	0.6070	0.0016	0.8507
0.2795	5.0	1715	0.2883	0.6264	0.7025	0.8906	0.8675	0.9369	0.7303	0.0291	0.9489	0.7630	0.8689	0.6135	0.0283	0.8584
0.2215	6.0	2058	0.2845	0.6316	0.7081	0.8924	0.8873	0.9252	0.7457	0.0452	0.9373	0.7700	0.8700	0.6212	0.0431	0.8536
0.2372	7.0	2401	0.2770	0.6343	0.7197	0.8931	0.8565	0.9373	0.7906	0.0492	0.9651	0.7657	0.8715	0.6365	0.0472	0.8504
0.3055	8.0	2744	0.2742	0.6337	0.7201	0.8950	0.8835	0.9220	0.8026	0.0324	0.9603	0.7742	0.8728	0.6413	0.0317	0.8482
0.2047	9.0	3087	0.2680	0.6497	0.7251	0.8982	0.8733	0.9384	0.7786	0.0884	0.9468	0.7765	0.8766	0.6500	0.0819	0.8634
0.1705	10.0	3430	0.2675	0.6489	0.7328	0.8987	0.8744	0.9336	0.8043	0.0862	0.9654	0.7793	0.8767	0.6531	0.0802	0.8550
0.2029	11.0	3773	0.2685	0.6523	0.7267	0.9003	0.8751	0.9443	0.7596	0.0958	0.9589	0.7812	0.8779	0.6536	0.0890	0.8600
0.1707	12.0	4116	0.2612	0.6591	0.7360	0.9015	0.8866	0.9324	0.7982	0.1097	0.9532	0.7853	0.8788	0.6639	0.0995	0.8679
0.2742	13.0	4459	0.2628	0.6512	0.7247	0.9022	0.8756	0.9442	0.7781	0.0666	0.9593	0.7847	0.8797	0.6635	0.0633	0.8651
0.2991	14.0	4802	0.2702	0.6653	0.7404	0.9025	0.8909	0.9368	0.7673	0.1492	0.9578	0.7870	0.8799	0.6627	0.1247	0.8722
0.229	15.0	5145	0.2599	0.6615	0.7395	0.9026	0.8723	0.9463	0.7800	0.1303	0.9687	0.7850	0.8798	0.6682	0.1143	0.8604
0.2004	16.0	5488	0.2595	0.6719	0.7473	0.9042	0.8854	0.9398	0.7863	0.1735	0.9513	0.7898	0.8814	0.6719	0.1442	0.8721
0.1944	17.0	5831	0.2564	0.6729	0.7486	0.9058	0.8940	0.9368	0.7895	0.1693	0.9536	0.7936	0.8830	0.6778	0.1418	0.8685
0.2068	18.0	6174	0.2539	0.6664	0.7450	0.9061	0.8915	0.9362	0.8051	0.1245	0.9677	0.7940	0.8839	0.6801	0.1102	0.8641
0.2461	19.0	6517	0.2494	0.6776	0.7603	0.9063	0.8756	0.9427	0.8251	0.1941	0.9642	0.7927	0.8854	0.6800	0.1585	0.8712
0.2252	20.0	6860	0.2498	0.6733	0.7461	0.9074	0.8813	0.9452	0.8043	0.1456	0.9542	0.7947	0.8856	0.6843	0.1284	0.8736
0.1975	21.0	7203	0.2519	0.6761	0.7516	0.9084	0.8960	0.9386	0.7992	0.1656	0.9585	0.7989	0.8861	0.6862	0.1412	0.8679
0.2356	22.0	7546	0.2506	0.6801	0.7526	0.9087	0.8956	0.9396	0.7972	0.1764	0.9542	0.7991	0.8858	0.6890	0.1486	0.8779
0.1838	23.0	7889	0.2510	0.6805	0.7554	0.9088	0.8835	0.9455	0.8068	0.1824	0.9589	0.7978	0.8867	0.6892	0.1516	0.8773
0.1576	24.0	8232	0.2511	0.6850	0.7658	0.9091	0.8913	0.9418	0.8021	0.2291	0.9650	0.7996	0.8868	0.6891	0.1765	0.8731
0.1504	25.0	8575	0.2505	0.6819	0.7590	0.9092	0.8869	0.9439	0.8077	0.1916	0.9650	0.7992	0.8873	0.6890	0.1587	0.8751
0.2196	26.0	8918	0.2530	0.6830	0.7597	0.9095	0.8946	0.9405	0.8035	0.1985	0.9612	0.8010	0.8872	0.6900	0.1610	0.8756
0.1781	27.0	9261	0.2509	0.6841	0.7596	0.9101	0.8901	0.9451	0.7993	0.2000	0.9635	0.8010	0.8880	0.6930	0.1635	0.8749
0.1578	28.0	9604	0.2485	0.6831	0.7591	0.9102	0.8874	0.9457	0.8064	0.1912	0.9651	0.8008	0.8882	0.6942	0.1585	0.8740
0.1931	29.0	9947	0.2495	0.6840	0.7579	0.9105	0.8893	0.9454	0.8042	0.1899	0.9604	0.8016	0.8884	0.6940	0.1580	0.8779
0.1582	30.0	10290	0.2486	0.6867	0.7623	0.9106	0.8910	0.9442	0.8061	0.2109	0.9591	0.8022	0.8886	0.6946	0.1697	0.8786

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

ironchanchellor/segformer-b0_DsMetalDam_Augmented_Cropped

segformer-b0_DsMetalDam_Augmented_Cropped

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ironchanchellor/
segformer-b0_DsMetalDam_Augmented_Cropped