smids_5x_deit_tiny_adamax_00001_fold2

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: 1.0539
• Accuracy: 0.8752

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
0.3245	1.0	375	0.3357	0.8586
0.2435	2.0	750	0.3012	0.8802
0.1837	3.0	1125	0.3092	0.8802
0.0922	4.0	1500	0.3362	0.8719
0.064	5.0	1875	0.4063	0.8619
0.0948	6.0	2250	0.4674	0.8619
0.0452	7.0	2625	0.5334	0.8602
0.0373	8.0	3000	0.6077	0.8619
0.0111	9.0	3375	0.6364	0.8769
0.0018	10.0	3750	0.7083	0.8636
0.0038	11.0	4125	0.7404	0.8752
0.0175	12.0	4500	0.8300	0.8719
0.0012	13.0	4875	0.8986	0.8652
0.0087	14.0	5250	0.8825	0.8686
0.004	15.0	5625	0.8822	0.8785
0.0001	16.0	6000	0.9237	0.8735
0.0162	17.0	6375	0.9830	0.8619
0.0	18.0	6750	1.0120	0.8702
0.0	19.0	7125	1.0192	0.8719
0.0001	20.0	7500	0.9781	0.8735
0.0	21.0	7875	1.0188	0.8702
0.0	22.0	8250	0.9776	0.8735
0.0	23.0	8625	1.0494	0.8702
0.0	24.0	9000	0.9531	0.8752
0.0	25.0	9375	1.0293	0.8719
0.0	26.0	9750	1.0427	0.8652
0.0	27.0	10125	1.0483	0.8719
0.0	28.0	10500	1.0202	0.8735
0.0	29.0	10875	1.0779	0.8686
0.0	30.0	11250	1.0065	0.8719
0.0018	31.0	11625	1.0762	0.8702
0.0202	32.0	12000	1.0874	0.8669
0.0024	33.0	12375	1.0366	0.8735
0.0	34.0	12750	1.1165	0.8686
0.0	35.0	13125	1.0244	0.8752
0.0	36.0	13500	1.1014	0.8719
0.0	37.0	13875	1.0995	0.8702
0.0	38.0	14250	1.1070	0.8719
0.0	39.0	14625	1.0209	0.8769
0.0048	40.0	15000	1.0540	0.8752
0.0	41.0	15375	1.0624	0.8752
0.0015	42.0	15750	1.0637	0.8752
0.0013	43.0	16125	1.0536	0.8752
0.0013	44.0	16500	1.0479	0.8752
0.0013	45.0	16875	1.0540	0.8752
0.0	46.0	17250	1.0694	0.8752
0.0016	47.0	17625	1.0601	0.8752
0.0	48.0	18000	1.0596	0.8752
0.0013	49.0	18375	1.0574	0.8752
0.0012	50.0	18750	1.0539	0.8752

Framework versions

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