smids_5x_deit_tiny_sgd_00001_fold1

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

• Loss: 1.0635
• Accuracy: 0.4541

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
1.346	1.0	376	1.2991	0.3489
1.3817	2.0	752	1.2686	0.3589
1.3103	3.0	1128	1.2425	0.3656
1.3556	4.0	1504	1.2205	0.3656
1.2443	5.0	1880	1.2020	0.3723
1.1947	6.0	2256	1.1865	0.3806
1.184	7.0	2632	1.1737	0.3940
1.2121	8.0	3008	1.1630	0.3873
1.1793	9.0	3384	1.1540	0.3773
1.1564	10.0	3760	1.1464	0.3740
1.148	11.0	4136	1.1397	0.3756
1.1774	12.0	4512	1.1340	0.3756
1.1493	13.0	4888	1.1288	0.3790
1.1491	14.0	5264	1.1241	0.3790
1.1465	15.0	5640	1.1198	0.3856
1.1089	16.0	6016	1.1159	0.3990
1.1015	17.0	6392	1.1122	0.4057
1.1166	18.0	6768	1.1086	0.4073
1.1502	19.0	7144	1.1053	0.4124
1.124	20.0	7520	1.1022	0.4174
1.1102	21.0	7896	1.0992	0.4207
1.0904	22.0	8272	1.0964	0.4190
1.0897	23.0	8648	1.0937	0.4207
1.1449	24.0	9024	1.0912	0.4190
1.0609	25.0	9400	1.0888	0.4157
1.0747	26.0	9776	1.0865	0.4207
1.0631	27.0	10152	1.0844	0.4240
1.0872	28.0	10528	1.0823	0.4274
1.0811	29.0	10904	1.0804	0.4290
1.1082	30.0	11280	1.0786	0.4307
1.0863	31.0	11656	1.0769	0.4324
1.103	32.0	12032	1.0753	0.4290
1.0918	33.0	12408	1.0738	0.4324
1.06	34.0	12784	1.0725	0.4391
1.0723	35.0	13160	1.0712	0.4424
1.0366	36.0	13536	1.0701	0.4457
1.0655	37.0	13912	1.0690	0.4474
1.0787	38.0	14288	1.0681	0.4457
1.0751	39.0	14664	1.0672	0.4474
1.0508	40.0	15040	1.0665	0.4541
1.0565	41.0	15416	1.0658	0.4541
1.0404	42.0	15792	1.0652	0.4541
1.0767	43.0	16168	1.0648	0.4541
1.076	44.0	16544	1.0644	0.4541
1.0183	45.0	16920	1.0640	0.4541
1.0393	46.0	17296	1.0638	0.4541
1.065	47.0	17672	1.0636	0.4541
1.0432	48.0	18048	1.0635	0.4541
1.0432	49.0	18424	1.0635	0.4541
1.0255	50.0	18800	1.0635	0.4541

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.1+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2