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
0.4826	1.0	327	0.4735	0.5267	0.6425	0.8330	0.7351	0.9126	0.7551	0.0	0.8096	0.6604	0.8334	0.4020	0.0	0.7378
0.5204	2.0	654	0.3567	0.5622	0.6354	0.8720	0.8799	0.9316	0.4736	0.0	0.8919	0.7422	0.8521	0.4239	0.0	0.7931
0.1559	3.0	981	0.3234	0.5879	0.6646	0.8797	0.8867	0.9260	0.5670	0.0	0.9436	0.7525	0.8583	0.4930	0.0	0.8354
0.2957	4.0	1308	0.3037	0.5972	0.6781	0.8862	0.8723	0.9393	0.6163	0.0	0.9626	0.7625	0.8649	0.5289	0.0	0.8298
0.2695	5.0	1635	0.2920	0.6007	0.6923	0.8890	0.8703	0.9338	0.6929	0.0	0.9646	0.7681	0.8680	0.5577	0.0	0.8097
0.7461	6.0	1962	0.2805	0.6137	0.7045	0.8923	0.8600	0.9370	0.7549	0.0	0.9703	0.7724	0.8711	0.5850	0.0	0.8397
0.1637	7.0	2289	0.2660	0.6245	0.6967	0.8982	0.8940	0.9342	0.7024	0.0013	0.9516	0.7861	0.8753	0.5952	0.0013	0.8648
0.1495	8.0	2616	0.2676	0.6240	0.7089	0.8966	0.8657	0.9373	0.7813	0.0052	0.9550	0.7796	0.8760	0.6039	0.0052	0.8555
0.2658	9.0	2943	0.2620	0.6345	0.7127	0.9005	0.8747	0.9403	0.7699	0.0257	0.9529	0.7886	0.8772	0.6204	0.0250	0.8613
0.2517	10.0	3270	0.2532	0.6529	0.7218	0.9040	0.8884	0.9394	0.7607	0.0885	0.9321	0.7964	0.8797	0.6333	0.0799	0.8753
0.152	11.0	3597	0.2490	0.6606	0.7273	0.9052	0.8820	0.9475	0.7492	0.1412	0.9165	0.7971	0.8812	0.6373	0.1222	0.8652
0.4032	12.0	3924	0.2470	0.6606	0.7329	0.9062	0.8873	0.9406	0.7806	0.1045	0.9516	0.7997	0.8821	0.6464	0.0954	0.8796
0.1497	13.0	4251	0.2479	0.6608	0.7334	0.9069	0.8896	0.9415	0.7729	0.1016	0.9615	0.8015	0.8823	0.6487	0.0917	0.8797
0.2648	14.0	4578	0.2441	0.6580	0.7258	0.9081	0.8954	0.9399	0.7758	0.0742	0.9434	0.8048	0.8836	0.6521	0.0654	0.8841
0.2517	15.0	4905	0.2389	0.6858	0.7683	0.9101	0.8936	0.9468	0.7543	0.2911	0.9555	0.8077	0.8863	0.6557	0.1957	0.8838
0.4088	16.0	5232	0.2344	0.6849	0.7577	0.9116	0.8983	0.9425	0.7828	0.2164	0.9488	0.8104	0.8872	0.6676	0.1772	0.8821
0.7573	17.0	5559	0.2329	0.6893	0.7678	0.9127	0.8986	0.9443	0.7803	0.2521	0.9634	0.8126	0.8887	0.6713	0.1922	0.8818
0.361	18.0	5886	0.2352	0.6844	0.7547	0.9129	0.9003	0.9458	0.7686	0.2055	0.9534	0.8136	0.8890	0.6658	0.1672	0.8865
0.0933	19.0	6213	0.2302	0.6929	0.7691	0.9132	0.8960	0.9423	0.8121	0.2513	0.9435	0.8143	0.8891	0.6750	0.1950	0.8910
0.2342	20.0	6540	0.2325	0.6805	0.7436	0.9134	0.8885	0.9530	0.7799	0.1512	0.9456	0.8130	0.8886	0.6758	0.1313	0.8936
0.0977	21.0	6867	0.2303	0.6927	0.7679	0.9147	0.9033	0.9437	0.7897	0.2524	0.9502	0.8178	0.8902	0.6791	0.1890	0.8876
0.144	22.0	7194	0.2277	0.6921	0.7674	0.9146	0.8850	0.9539	0.7986	0.2441	0.9555	0.8149	0.8913	0.6788	0.1885	0.8872
0.093	23.0	7521	0.2298	0.6911	0.7612	0.9154	0.9035	0.9431	0.8014	0.2015	0.9564	0.8192	0.8910	0.6811	0.1643	0.8998
0.1885	24.0	7848	0.2231	0.6910	0.7651	0.9166	0.8901	0.9513	0.8184	0.1993	0.9667	0.8190	0.8933	0.6910	0.1647	0.8868
0.1533	25.0	8175	0.2208	0.6946	0.7786	0.9169	0.8939	0.9477	0.8263	0.2488	0.9765	0.8202	0.8937	0.6930	0.1962	0.8698
0.1924	26.0	8502	0.2250	0.6989	0.7688	0.9173	0.9060	0.9462	0.7923	0.2436	0.9558	0.8219	0.8941	0.6853	0.1948	0.8983
0.1499	27.0	8829	0.2227	0.7001	0.7698	0.9178	0.8955	0.9508	0.8132	0.2381	0.9511	0.8224	0.8939	0.6937	0.1901	0.9003
0.2098	28.0	9156	0.2212	0.7053	0.7788	0.9181	0.8947	0.9519	0.8127	0.2950	0.9395	0.8227	0.8947	0.6942	0.2184	0.8967
0.0973	29.0	9483	0.2181	0.6930	0.7643	0.9192	0.9014	0.9476	0.8256	0.1744	0.9723	0.8248	0.8957	0.7018	0.1574	0.8851
0.1031	30.0	9810	0.2197	0.7056	0.7822	0.9188	0.9089	0.9440	0.8100	0.2872	0.9610	0.8257	0.8946	0.6971	0.2147	0.8961
0.0886	31.0	10137	0.2221	0.7089	0.7898	0.9187	0.8989	0.9498	0.8115	0.3236	0.9653	0.8248	0.8948	0.6962	0.2319	0.8966
0.1318	32.0	10464	0.2216	0.7048	0.7767	0.9194	0.9103	0.9463	0.7981	0.2698	0.9590	0.8272	0.8949	0.6981	0.2085	0.8954
0.1206	33.0	10791	0.2188	0.7082	0.7860	0.9196	0.8973	0.9502	0.8280	0.2927	0.9617	0.8264	0.8964	0.6989	0.2195	0.8999
0.2152	34.0	11118	0.2179	0.7112	0.7846	0.9198	0.8937	0.9537	0.8234	0.2997	0.9523	0.8254	0.8963	0.7034	0.2279	0.9029
0.1581	35.0	11445	0.2170	0.7083	0.7839	0.9204	0.9043	0.9495	0.8116	0.2810	0.9729	0.8278	0.8970	0.7021	0.2257	0.8890
0.3181	36.0	11772	0.2185	0.7097	0.7850	0.9202	0.9128	0.9443	0.8080	0.2957	0.9641	0.8289	0.8960	0.7016	0.2241	0.8977
0.3164	37.0	12099	0.2172	0.7135	0.7907	0.9208	0.9103	0.9470	0.8074	0.3300	0.9587	0.8300	0.8969	0.7023	0.2379	0.9003
0.1082	38.0	12426	0.2180	0.7131	0.7859	0.9207	0.8981	0.9530	0.8199	0.3055	0.9530	0.8283	0.8973	0.7029	0.2345	0.9023
0.1884	39.0	12753	0.2187	0.7115	0.7851	0.9207	0.9010	0.9508	0.8223	0.2911	0.9604	0.8291	0.8969	0.7038	0.2256	0.9018
0.1169	40.0	13080	0.2171	0.7130	0.7858	0.9215	0.9066	0.9497	0.8146	0.2965	0.9616	0.8309	0.8978	0.7051	0.2286	0.9026
0.1622	41.0	13407	0.2160	0.7114	0.7808	0.9215	0.9020	0.9512	0.8260	0.2715	0.9532	0.8304	0.8979	0.7068	0.2183	0.9035
0.2682	42.0	13734	0.2164	0.7120	0.7840	0.9219	0.9054	0.9496	0.8255	0.2751	0.9647	0.8313	0.8985	0.7077	0.2206	0.9018
0.1214	43.0	14061	0.2159	0.7044	0.7717	0.9217	0.9083	0.9485	0.8199	0.2176	0.9641	0.8315	0.8978	0.7072	0.1835	0.9020
0.1344	44.0	14388	0.2153	0.7145	0.7906	0.9220	0.9039	0.9506	0.8251	0.3057	0.9677	0.8318	0.8985	0.7080	0.2338	0.9006
0.1233	45.0	14715	0.2142	0.7135	0.7859	0.9220	0.9063	0.9488	0.8285	0.2853	0.9606	0.8319	0.8986	0.7084	0.2253	0.9030
0.1819	46.0	15042	0.2150	0.7156	0.7878	0.9224	0.9058	0.9504	0.8244	0.2985	0.9601	0.8324	0.8989	0.7098	0.2327	0.9042
0.1338	47.0	15369	0.2159	0.7130	0.7846	0.9223	0.9080	0.9502	0.8151	0.2853	0.9646	0.8328	0.8987	0.7072	0.2247	0.9015
0.1299	48.0	15696	0.2129	0.7158	0.7882	0.9224	0.9017	0.9524	0.8288	0.2965	0.9617	0.8319	0.8989	0.7113	0.2336	0.9030
0.1295	49.0	16023	0.2163	0.7147	0.7854	0.9224	0.9120	0.9477	0.8157	0.2948	0.9567	0.8332	0.8986	0.7081	0.2296	0.9042
0.1567	50.0	16350	0.2158	0.7158	0.7885	0.9224	0.9019	0.9539	0.8174	0.3067	0.9625	0.8320	0.8988	0.7096	0.2363	0.9024

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

ironchanchellor/segformer-b0_DsMetalDam_Train_Augmented_Cropped

segformer-b0_DsMetalDam_Train_Augmented_Cropped

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ironchanchellor/
segformer-b0_DsMetalDam_Train_Augmented_Cropped