smids_10x_deit_tiny_sgd_00001_fold4

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: 0.9601
• Accuracy: 0.51

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
1.4156	1.0	750	1.2978	0.34
1.3315	2.0	1500	1.2425	0.3483
1.2993	3.0	2250	1.2021	0.37
1.2642	4.0	3000	1.1733	0.3717
1.1084	5.0	3750	1.1526	0.375
1.1915	6.0	4500	1.1373	0.3733
1.1121	7.0	5250	1.1248	0.3817
1.1023	8.0	6000	1.1144	0.39
1.0611	9.0	6750	1.1051	0.3867
1.0698	10.0	7500	1.0965	0.39
1.0512	11.0	8250	1.0884	0.4017
1.0962	12.0	9000	1.0808	0.405
1.0873	13.0	9750	1.0735	0.4117
1.0536	14.0	10500	1.0664	0.4183
1.0525	15.0	11250	1.0596	0.4283
1.026	16.0	12000	1.0532	0.4317
1.0131	17.0	12750	1.0470	0.44
0.9786	18.0	13500	1.0410	0.4433
0.9869	19.0	14250	1.0353	0.4467
0.9996	20.0	15000	1.0299	0.4517
1.0078	21.0	15750	1.0247	0.4533
0.9709	22.0	16500	1.0197	0.4617
1.009	23.0	17250	1.0149	0.4633
1.0068	24.0	18000	1.0104	0.4633
0.9737	25.0	18750	1.0061	0.47
0.9634	26.0	19500	1.0021	0.4767
0.9648	27.0	20250	0.9982	0.4783
0.931	28.0	21000	0.9946	0.485
0.993	29.0	21750	0.9911	0.4867
0.9852	30.0	22500	0.9879	0.49
0.9579	31.0	23250	0.9848	0.49
0.9747	32.0	24000	0.9819	0.4933
0.9501	33.0	24750	0.9793	0.5017
0.9432	34.0	25500	0.9768	0.5033
0.9384	35.0	26250	0.9745	0.505
0.9356	36.0	27000	0.9724	0.505
0.9023	37.0	27750	0.9705	0.5067
0.9257	38.0	28500	0.9687	0.5083
0.9635	39.0	29250	0.9672	0.5083
0.9335	40.0	30000	0.9658	0.51
0.8943	41.0	30750	0.9645	0.51
0.9485	42.0	31500	0.9635	0.51
0.976	43.0	32250	0.9626	0.51
0.9386	44.0	33000	0.9619	0.51
0.9526	45.0	33750	0.9613	0.51
0.9016	46.0	34500	0.9608	0.51
0.9008	47.0	35250	0.9605	0.51
0.9525	48.0	36000	0.9603	0.51
0.8965	49.0	36750	0.9602	0.51
0.8897	50.0	37500	0.9601	0.51

Framework versions

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