smids_5x_deit_small_sgd_00001_fold1

This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.9977
• Accuracy: 0.5159

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: 1e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.0592	1.0	376	1.0737	0.4391
1.0782	2.0	752	1.0706	0.4374
1.0408	3.0	1128	1.0676	0.4391
1.0826	4.0	1504	1.0646	0.4441
1.0586	5.0	1880	1.0616	0.4407
1.0333	6.0	2256	1.0588	0.4424
1.0668	7.0	2632	1.0559	0.4424
1.0617	8.0	3008	1.0531	0.4441
1.0464	9.0	3384	1.0504	0.4457
1.0296	10.0	3760	1.0477	0.4474
1.0219	11.0	4136	1.0452	0.4524
1.036	12.0	4512	1.0426	0.4558
1.0086	13.0	4888	1.0402	0.4591
1.0374	14.0	5264	1.0378	0.4591
1.0308	15.0	5640	1.0354	0.4624
1.0138	16.0	6016	1.0332	0.4641
1.039	17.0	6392	1.0310	0.4674
1.0251	18.0	6768	1.0289	0.4691
1.0132	19.0	7144	1.0268	0.4674
1.0078	20.0	7520	1.0248	0.4674
1.0073	21.0	7896	1.0229	0.4741
0.9973	22.0	8272	1.0210	0.4775
0.9979	23.0	8648	1.0192	0.4791
0.9943	24.0	9024	1.0175	0.4791
0.9653	25.0	9400	1.0159	0.4841
0.9982	26.0	9776	1.0143	0.4841
1.0041	27.0	10152	1.0128	0.4875
1.0054	28.0	10528	1.0114	0.4908
0.9643	29.0	10904	1.0101	0.4925
0.9735	30.0	11280	1.0088	0.4958
1.0	31.0	11656	1.0076	0.4958
0.998	32.0	12032	1.0064	0.4975
0.9763	33.0	12408	1.0054	0.4975
0.9704	34.0	12784	1.0044	0.4992
0.9948	35.0	13160	1.0035	0.5008
0.9708	36.0	13536	1.0026	0.5008
0.9711	37.0	13912	1.0019	0.5025
0.999	38.0	14288	1.0012	0.5042
0.9534	39.0	14664	1.0005	0.5042
0.9776	40.0	15040	1.0000	0.5058
1.0022	41.0	15416	0.9995	0.5058
0.9618	42.0	15792	0.9991	0.5058
0.9978	43.0	16168	0.9987	0.5109
0.9845	44.0	16544	0.9984	0.5142
0.9783	45.0	16920	0.9982	0.5159
0.99	46.0	17296	0.9980	0.5159
0.9708	47.0	17672	0.9979	0.5159
1.0004	48.0	18048	0.9978	0.5159
0.9871	49.0	18424	0.9977	0.5159
0.9947	50.0	18800	0.9977	0.5159

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2