smids_10x_deit_tiny_adamax_001_fold3

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: 1.0576
• Accuracy: 0.9133

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
0.366	1.0	750	0.3478	0.86
0.3154	2.0	1500	0.3552	0.8633
0.2007	3.0	2250	0.4696	0.845
0.2296	4.0	3000	0.3387	0.8783
0.2607	5.0	3750	0.4239	0.865
0.2365	6.0	4500	0.3514	0.895
0.1244	7.0	5250	0.3538	0.8917
0.1356	8.0	6000	0.3984	0.895
0.0526	9.0	6750	0.4940	0.875
0.1195	10.0	7500	0.5287	0.8783
0.0559	11.0	8250	0.6054	0.8817
0.0512	12.0	9000	0.6374	0.8717
0.0498	13.0	9750	0.6405	0.8817
0.0296	14.0	10500	0.6601	0.895
0.0626	15.0	11250	0.7807	0.89
0.055	16.0	12000	0.7694	0.905
0.0444	17.0	12750	0.6413	0.905
0.0317	18.0	13500	0.7330	0.9033
0.0108	19.0	14250	0.7464	0.8917
0.0236	20.0	15000	0.7591	0.885
0.01	21.0	15750	0.8264	0.9067
0.0226	22.0	16500	0.7921	0.8933
0.0127	23.0	17250	0.7486	0.9033
0.0025	24.0	18000	0.8018	0.8983
0.0004	25.0	18750	0.7411	0.9083
0.0	26.0	19500	0.8554	0.895
0.0	27.0	20250	0.9122	0.9017
0.0	28.0	21000	0.8611	0.9067
0.0041	29.0	21750	0.8741	0.9033
0.0	30.0	22500	0.7969	0.9167
0.012	31.0	23250	0.8521	0.91
0.0058	32.0	24000	0.9974	0.8983
0.0	33.0	24750	0.9864	0.9
0.0	34.0	25500	0.8709	0.91
0.0	35.0	26250	0.9411	0.9117
0.0	36.0	27000	1.0050	0.9033
0.0	37.0	27750	0.9456	0.905
0.0	38.0	28500	0.9323	0.9083
0.0	39.0	29250	0.9349	0.9117
0.0	40.0	30000	0.9420	0.9117
0.0	41.0	30750	0.9601	0.9133
0.0	42.0	31500	0.9780	0.9133
0.0	43.0	32250	0.9953	0.9133
0.0	44.0	33000	1.0029	0.915
0.0	45.0	33750	1.0208	0.9133
0.0	46.0	34500	1.0335	0.9133
0.0	47.0	35250	1.0420	0.9133
0.0	48.0	36000	1.0522	0.9133
0.0	49.0	36750	1.0572	0.9117
0.0	50.0	37500	1.0576	0.9133

Framework versions

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