segformer-b0_DsMetalDam_Train_Augmented_Cropped

ironchanchellor/segformer-b0_DsMetalDam_Train_Augmented_Cropped

segformer-b0_DsMetalDam_Train_Augmented_Cropped is a machine learning model from ironchanchellor. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: other
Model type: segformer
Access: Public
Created: Sep 26, 2023
Updated: Sep 26, 2023
SHA: 92ca2635

Base models

nvidia/mit-b0

Model card

From the Hugging Face model README

segformer-b0_DsMetalDam_Train_Augmented_Cropped

This model is a fine-tuned version of nvidia/mit-b0 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2158
Mean Iou: 0.7158
Mean Accuracy: 0.7885
Overall Accuracy: 0.9224
Accuracy Matrix: 0.9019
Accuracy Austenite: 0.9539
Accuracy Martensite/austenite: 0.8174
Accuracy Precipitate: 0.3067
Accuracy Defect: 0.9625
Iou Matrix: 0.8320
Iou Austenite: 0.8988
Iou Martensite/austenite: 0.7096
Iou Precipitate: 0.2363
Iou Defect: 0.9024

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 6e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

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

Library: transformers
Type: segformer
License: other
Created: Sep 26, 2023
Updated: Sep 26, 2023