testing_100_epoches

Transformers123/testing_100_epoches

testing_100_epoches is a machine learning model from Transformers123. 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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Hugging Face

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At a glance

Library: transformers
License: other
Model type: segformer
Access: Public
Created: Oct 2, 2023
Updated: Oct 5, 2023
SHA: a4a6273e

Base models

nvidia/mit-b2

Model card

From the Hugging Face model README

testing_100_epoches

This model is a fine-tuned version of nvidia/mit-b2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1400
Mean Iou: 0.0066
Mean Accuracy: 0.0133
Overall Accuracy: 0.0133
Accuracy Bkg: nan
Accuracy Wht: 0.0133
Iou Bkg: 0.0
Iou Wht: 0.0133

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: 3
eval_batch_size: 8
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 Bkg	Accuracy Wht	Iou Wht
0.1707	1.0	180	0.0800	0.0	0.0	0.0	nan	0.0	0.0
0.0919	2.0	360	0.0798	0.0	0.0	0.0	nan	0.0	0.0
0.0874	3.0	540	0.0771	0.0	0.0	0.0	nan	0.0	0.0
0.0843	4.0	720	0.0786	0.0	0.0	0.0	nan	0.0	0.0
0.0808	5.0	900	0.0805	0.0014	0.0029	0.0029	nan	0.0029	0.0029
0.0775	6.0	1080	0.0796	0.0	0.0	0.0	nan	0.0	0.0
0.0712	7.0	1260	0.0906	0.0381	0.0762	0.0762	nan	0.0762	0.0762
0.0629	8.0	1440	0.0830	0.0055	0.0110	0.0110	nan	0.0110	0.0110
0.0547	9.0	1620	0.0911	0.0141	0.0281	0.0281	nan	0.0281	0.0281
0.0483	10.0	1800	0.1032	0.0097	0.0194	0.0194	nan	0.0194	0.0194
0.0443	11.0	1980	0.0924	0.0146	0.0292	0.0292	nan	0.0292	0.0292
0.0406	12.0	2160	0.0979	0.0069	0.0137	0.0137	nan	0.0137	0.0137
0.0369	13.0	2340	0.1030	0.0103	0.0206	0.0206	nan	0.0206	0.0206
0.0352	14.0	2520	0.0993	0.0065	0.0129	0.0129	nan	0.0129	0.0129
0.0341	15.0	2700	0.0978	0.0062	0.0125	0.0125	nan	0.0125	0.0125
0.0324	16.0	2880	0.1044	0.0151	0.0302	0.0302	nan	0.0302	0.0302
0.0307	17.0	3060	0.1014	0.0164	0.0328	0.0328	nan	0.0328	0.0328
0.03	18.0	3240	0.1043	0.0128	0.0257	0.0257	nan	0.0257	0.0257
0.0295	19.0	3420	0.1093	0.0083	0.0165	0.0165	nan	0.0165	0.0165
0.0273	20.0	3600	0.1136	0.0100	0.0201	0.0201	nan	0.0201	0.0201
0.0264	21.0	3780	0.1086	0.0154	0.0309	0.0309	nan	0.0309	0.0309
0.0261	22.0	3960	0.1107	0.0165	0.0330	0.0330	nan	0.0330	0.0330
0.0257	23.0	4140	0.1119	0.0137	0.0274	0.0274	nan	0.0274	0.0274
0.0248	24.0	4320	0.1140	0.0101	0.0201	0.0201	nan	0.0201	0.0201
0.0242	25.0	4500	0.1056	0.0168	0.0336	0.0336	nan	0.0336	0.0336
0.024	26.0	4680	0.1143	0.0100	0.0200	0.0200	nan	0.0200	0.0200
0.0234	27.0	4860	0.1155	0.0091	0.0181	0.0181	nan	0.0181	0.0181
0.0228	28.0	5040	0.1201	0.0073	0.0146	0.0146	nan	0.0146	0.0146
0.0226	29.0	5220	0.1192	0.0094	0.0188	0.0188	nan	0.0188	0.0188
0.0224	30.0	5400	0.1187	0.0118	0.0237	0.0237	nan	0.0237	0.0237
0.0218	31.0	5580	0.1227	0.0105	0.0209	0.0209	nan	0.0209	0.0209
0.0211	32.0	5760	0.1159	0.0155	0.0310	0.0310	nan	0.0310	0.0310
0.0208	33.0	5940	0.1224	0.0108	0.0215	0.0215	nan	0.0215	0.0215
0.0203	34.0	6120	0.1239	0.0123	0.0246	0.0246	nan	0.0246	0.0246
0.0197	35.0	6300	0.1285	0.0065	0.0130	0.0130	nan	0.0130	0.0130
0.02	36.0	6480	0.1293	0.0037	0.0075	0.0075	nan	0.0075	0.0075
0.0192	37.0	6660	0.1258	0.0059	0.0119	0.0119	nan	0.0119	0.0119
0.0193	38.0	6840	0.1234	0.0105	0.0210	0.0210	nan	0.0210	0.0210
0.0189	39.0	7020	0.1267	0.0080	0.0159	0.0159	nan	0.0159	0.0159
0.0181	40.0	7200	0.1308	0.0060	0.0120	0.0120	nan	0.0120	0.0120
0.0183	41.0	7380	0.1337	0.0056	0.0112	0.0112	nan	0.0112	0.0112
0.018	42.0	7560	0.1349	0.0071	0.0142	0.0142	nan	0.0142	0.0142
0.0178	43.0	7740	0.1332	0.0069	0.0139	0.0139	nan	0.0139	0.0139
0.0171	44.0	7920	0.1363	0.0066	0.0132	0.0132	nan	0.0132	0.0132
0.0176	45.0	8100	0.1352	0.0065	0.0131	0.0131	nan	0.0131	0.0131
0.0181	46.0	8280	0.1384	0.0064	0.0127	0.0127	nan	0.0127	0.0127
0.0173	47.0	8460	0.1419	0.0065	0.0129	0.0129	nan	0.0129	0.0129
0.0176	48.0	8640	0.1374	0.0081	0.0161	0.0161	nan	0.0161	0.0161
0.0173	49.0	8820	0.1383	0.0065	0.0130	0.0130	nan	0.0130	0.0130
0.0173	50.0	9000	0.1400	0.0066	0.0133	0.0133	nan	0.0133	0.0133

Framework versions

Transformers 4.33.3
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

Library: transformers
Type: segformer
License: other
Created: Oct 2, 2023
Updated: Oct 5, 2023