smids_5x_deit_small_rms_00001_fold4

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: 1.4091
• Accuracy: 0.8733

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
0.2357	1.0	375	0.3537	0.8617
0.1744	2.0	750	0.3961	0.8667
0.086	3.0	1125	0.5528	0.8517
0.0758	4.0	1500	0.6078	0.87
0.0324	5.0	1875	0.7790	0.8683
0.0181	6.0	2250	0.9636	0.855
0.0003	7.0	2625	0.8894	0.8633
0.0164	8.0	3000	1.0314	0.865
0.0386	9.0	3375	1.0455	0.87
0.0021	10.0	3750	1.0868	0.86
0.0004	11.0	4125	1.1036	0.8667
0.0001	12.0	4500	1.0921	0.8683
0.0451	13.0	4875	1.1416	0.865
0.0313	14.0	5250	1.1480	0.875
0.0	15.0	5625	1.2563	0.8617
0.0294	16.0	6000	1.1827	0.8683
0.0014	17.0	6375	1.1784	0.8683
0.0017	18.0	6750	1.2479	0.8633
0.0	19.0	7125	1.1339	0.8783
0.0	20.0	7500	1.1679	0.8783
0.0	21.0	7875	1.2344	0.8683
0.0027	22.0	8250	1.2529	0.8667
0.0001	23.0	8625	1.2449	0.865
0.0	24.0	9000	1.2744	0.865
0.0177	25.0	9375	1.2614	0.8717
0.0	26.0	9750	1.2469	0.8717
0.0	27.0	10125	1.3072	0.8667
0.0	28.0	10500	1.2917	0.8683
0.0	29.0	10875	1.2871	0.8767
0.0	30.0	11250	1.3156	0.8617
0.0	31.0	11625	1.2509	0.8767
0.0	32.0	12000	1.2613	0.8783
0.0	33.0	12375	1.3454	0.8683
0.0001	34.0	12750	1.2978	0.8667
0.0	35.0	13125	1.2980	0.8617
0.0	36.0	13500	1.2859	0.8683
0.0	37.0	13875	1.3447	0.8683
0.0044	38.0	14250	1.3496	0.87
0.0	39.0	14625	1.3716	0.8717
0.0	40.0	15000	1.3540	0.8733
0.0	41.0	15375	1.3616	0.8733
0.0	42.0	15750	1.3855	0.8717
0.0	43.0	16125	1.3813	0.8733
0.0	44.0	16500	1.4013	0.8717
0.0	45.0	16875	1.3939	0.875
0.0	46.0	17250	1.3982	0.875
0.0	47.0	17625	1.4014	0.875
0.0	48.0	18000	1.4060	0.8733
0.0	49.0	18375	1.4084	0.8733
0.0	50.0	18750	1.4091	0.8733

Framework versions

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