smids_10x_deit_tiny_rms_0001_fold2

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: 1.7512
• Accuracy: 0.8469

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: 0.001
• 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
0.7916	1.0	750	0.7607	0.5990
0.7137	2.0	1500	0.7292	0.6922
0.7389	3.0	2250	0.6487	0.6905
0.641	4.0	3000	0.6309	0.7055
0.6314	5.0	3750	0.6856	0.6905
0.5224	6.0	4500	0.5750	0.7554
0.5522	7.0	5250	0.5498	0.7537
0.5542	8.0	6000	0.5375	0.7587
0.5077	9.0	6750	0.5514	0.7604
0.4953	10.0	7500	0.5328	0.7870
0.4839	11.0	8250	0.5735	0.7454
0.5867	12.0	9000	0.5344	0.7820
0.5019	13.0	9750	0.5724	0.7720
0.4158	14.0	10500	0.5203	0.7870
0.338	15.0	11250	0.5452	0.7903
0.3872	16.0	12000	0.4927	0.8070
0.3271	17.0	12750	0.5792	0.8070
0.3473	18.0	13500	0.4754	0.8203
0.3721	19.0	14250	0.4949	0.7887
0.3629	20.0	15000	0.4870	0.8136
0.3126	21.0	15750	0.4639	0.8303
0.2547	22.0	16500	0.5296	0.8236
0.3002	23.0	17250	0.5506	0.8003
0.2834	24.0	18000	0.5134	0.8186
0.2447	25.0	18750	0.6771	0.8053
0.211	26.0	19500	0.5840	0.8120
0.2265	27.0	20250	0.6139	0.8369
0.2018	28.0	21000	0.7828	0.8203
0.1385	29.0	21750	0.7229	0.8186
0.1626	30.0	22500	0.6948	0.8369
0.1046	31.0	23250	0.7987	0.8419
0.0986	32.0	24000	0.6857	0.8552
0.1014	33.0	24750	0.9042	0.8303
0.0734	34.0	25500	0.9163	0.8403
0.03	35.0	26250	0.9836	0.8353
0.0303	36.0	27000	0.9935	0.8436
0.0569	37.0	27750	1.1088	0.8286
0.0188	38.0	28500	1.2743	0.8319
0.0192	39.0	29250	1.3350	0.8386
0.032	40.0	30000	1.3569	0.8419
0.01	41.0	30750	1.2223	0.8353
0.0249	42.0	31500	1.4712	0.8436
0.0031	43.0	32250	1.5420	0.8403
0.0008	44.0	33000	1.5564	0.8419
0.026	45.0	33750	1.5884	0.8502
0.0017	46.0	34500	1.7455	0.8386
0.0001	47.0	35250	1.7636	0.8353
0.0001	48.0	36000	1.7269	0.8453
0.0	49.0	36750	1.7457	0.8453
0.0	50.0	37500	1.7512	0.8469

Framework versions

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