segformer-b1-solarModuleAnomaly-v0.1

This model is a fine-tuned version of nvidia/mit-b1 on the zklee98/solarModuleAnomaly dataset. It achieves the following results on the evaluation set:

• Loss: 0.1547
• Mean Iou: 0.3822
• Mean Accuracy: 0.7643
• Overall Accuracy: 0.7643
• Accuracy Unlabelled: nan
• Accuracy Anomaly: 0.7643
• Iou Unlabelled: 0.0
• Iou Anomaly: 0.7643

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: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabelled	Accuracy Anomaly	Iou Unlabelled	Iou Anomaly
0.4699	0.4	20	0.6337	0.4581	0.9162	0.9162	nan	0.9162	0.0	0.9162
0.3129	0.8	40	0.4636	0.3704	0.7407	0.7407	nan	0.7407	0.0	0.7407
0.2732	1.2	60	0.3164	0.3867	0.7734	0.7734	nan	0.7734	0.0	0.7734
0.2653	1.6	80	0.3769	0.4090	0.8180	0.8180	nan	0.8180	0.0	0.8180
0.2232	2.0	100	0.2976	0.2479	0.4958	0.4958	nan	0.4958	0.0	0.4958
0.5305	2.4	120	0.3151	0.3807	0.7613	0.7613	nan	0.7613	0.0	0.7613
0.2423	2.8	140	0.3189	0.4152	0.8305	0.8305	nan	0.8305	0.0	0.8305
0.3341	3.2	160	0.2384	0.3861	0.7723	0.7723	nan	0.7723	0.0	0.7723
0.2146	3.6	180	0.3200	0.4621	0.9243	0.9243	nan	0.9243	0.0	0.9243
0.1866	4.0	200	0.2510	0.3646	0.7291	0.7291	nan	0.7291	0.0	0.7291
0.2861	4.4	220	0.2736	0.4202	0.8404	0.8404	nan	0.8404	0.0	0.8404
0.2048	4.8	240	0.2410	0.3912	0.7823	0.7823	nan	0.7823	0.0	0.7823
0.1604	5.2	260	0.2233	0.3672	0.7344	0.7344	nan	0.7344	0.0	0.7344
0.2756	5.6	280	0.2705	0.4494	0.8987	0.8987	nan	0.8987	0.0	0.8987
0.1859	6.0	300	0.2211	0.4045	0.8089	0.8089	nan	0.8089	0.0	0.8089
0.1306	6.4	320	0.2140	0.3763	0.7525	0.7525	nan	0.7525	0.0	0.7525
0.5508	6.8	340	0.2231	0.4185	0.8371	0.8371	nan	0.8371	0.0	0.8371
0.1446	7.2	360	0.2139	0.3666	0.7332	0.7332	nan	0.7332	0.0	0.7332
0.3275	7.6	380	0.2470	0.3964	0.7928	0.7928	nan	0.7928	0.0	0.7928
0.164	8.0	400	0.2017	0.3910	0.7819	0.7819	nan	0.7819	0.0	0.7819
0.1864	8.4	420	0.2307	0.4408	0.8816	0.8816	nan	0.8816	0.0	0.8816
0.1578	8.8	440	0.1869	0.3707	0.7414	0.7414	nan	0.7414	0.0	0.7414
0.1201	9.2	460	0.2115	0.3834	0.7667	0.7667	nan	0.7667	0.0	0.7667
0.1783	9.6	480	0.2009	0.3747	0.7495	0.7495	nan	0.7495	0.0	0.7495
0.1232	10.0	500	0.1797	0.3865	0.7729	0.7729	nan	0.7729	0.0	0.7729
0.2572	10.4	520	0.1983	0.4057	0.8115	0.8115	nan	0.8115	0.0	0.8115
0.1209	10.8	540	0.1607	0.4274	0.8547	0.8547	nan	0.8547	0.0	0.8547
0.1234	11.2	560	0.2260	0.4066	0.8133	0.8133	nan	0.8133	0.0	0.8133
0.145	11.6	580	0.1963	0.3939	0.7878	0.7878	nan	0.7878	0.0	0.7878
0.0665	12.0	600	0.1912	0.3873	0.7747	0.7747	nan	0.7747	0.0	0.7747
0.0826	12.4	620	0.2095	0.4186	0.8373	0.8373	nan	0.8373	0.0	0.8373
0.1212	12.8	640	0.1732	0.4059	0.8118	0.8118	nan	0.8118	0.0	0.8118
0.142	13.2	660	0.2086	0.4007	0.8013	0.8013	nan	0.8013	0.0	0.8013
0.0899	13.6	680	0.1838	0.3928	0.7856	0.7856	nan	0.7856	0.0	0.7856
0.1857	14.0	700	0.1638	0.4157	0.8315	0.8315	nan	0.8315	0.0	0.8315
0.0788	14.4	720	0.1736	0.4112	0.8223	0.8223	nan	0.8223	0.0	0.8223
0.2543	14.8	740	0.1547	0.3822	0.7643	0.7643	nan	0.7643	0.0	0.7643

Framework versions

• Transformers 4.28.0
• Pytorch 2.0.0+cu118
• Datasets 2.11.0
• Tokenizers 0.13.3