hushem_1x_deit_tiny_rms_001_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: 1.0712
• Accuracy: 0.4762

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
No log	1.0	6	5.0165	0.2381
4.2481	2.0	12	3.3074	0.2381
4.2481	3.0	18	1.5288	0.2619
2.0024	4.0	24	1.5375	0.2381
1.6731	5.0	30	1.4069	0.2619
1.6731	6.0	36	1.8969	0.2381
1.5329	7.0	42	1.4811	0.2381
1.5329	8.0	48	1.4117	0.2619
1.475	9.0	54	1.4704	0.2619
1.4639	10.0	60	1.4459	0.2381
1.4639	11.0	66	1.3572	0.4524
1.4524	12.0	72	1.2630	0.4524
1.4524	13.0	78	1.2843	0.4524
1.4025	14.0	84	1.3420	0.2857
1.3666	15.0	90	1.4060	0.2381
1.3666	16.0	96	1.2621	0.3810
1.3178	17.0	102	1.2969	0.2857
1.3178	18.0	108	1.2881	0.3333
1.3667	19.0	114	1.3980	0.2857
1.3043	20.0	120	1.5195	0.2857
1.3043	21.0	126	1.1841	0.4048
1.2859	22.0	132	1.0567	0.5238
1.2859	23.0	138	1.2258	0.2619
1.2496	24.0	144	1.2372	0.2857
1.252	25.0	150	1.4386	0.3333
1.252	26.0	156	1.1416	0.3810
1.2296	27.0	162	1.0872	0.4286
1.2296	28.0	168	1.4121	0.2857
1.1581	29.0	174	1.0555	0.5476
1.2027	30.0	180	1.1296	0.4762
1.2027	31.0	186	1.2095	0.4048
1.1595	32.0	192	1.0821	0.4762
1.1595	33.0	198	1.1681	0.3810
1.1909	34.0	204	1.1147	0.4762
1.1121	35.0	210	1.0734	0.4048
1.1121	36.0	216	1.0002	0.5238
1.1218	37.0	222	1.1912	0.3095
1.1218	38.0	228	1.0883	0.4524
1.1024	39.0	234	1.1229	0.4286
1.0678	40.0	240	1.0903	0.4762
1.0678	41.0	246	1.0717	0.4762
1.058	42.0	252	1.0712	0.4762
1.058	43.0	258	1.0712	0.4762
1.0512	44.0	264	1.0712	0.4762
1.0743	45.0	270	1.0712	0.4762
1.0743	46.0	276	1.0712	0.4762
1.0691	47.0	282	1.0712	0.4762
1.0691	48.0	288	1.0712	0.4762
1.052	49.0	294	1.0712	0.4762
1.066	50.0	300	1.0712	0.4762

Framework versions

• Transformers 4.35.0
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1