hushem_1x_deit_tiny_rms_0001_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: 2.3903
• Accuracy: 0.5714

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: 0.0001
• 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	1.5699	0.2619
2.0801	2.0	12	1.5693	0.2381
2.0801	3.0	18	1.6087	0.2619
1.5352	4.0	24	1.4372	0.2619
1.4323	5.0	30	1.3212	0.3095
1.4323	6.0	36	1.3803	0.2381
1.3894	7.0	42	1.4606	0.4524
1.3894	8.0	48	1.5543	0.2619
1.294	9.0	54	1.1365	0.5
1.1627	10.0	60	1.3219	0.3571
1.1627	11.0	66	1.0508	0.5714
1.0159	12.0	72	1.0736	0.5
1.0159	13.0	78	1.6175	0.3571
0.8051	14.0	84	1.4409	0.4524
0.5869	15.0	90	2.1188	0.4286
0.5869	16.0	96	1.8546	0.5476
0.3044	17.0	102	1.7485	0.5
0.3044	18.0	108	1.6544	0.5476
0.2005	19.0	114	1.7817	0.5714
0.0634	20.0	120	2.6836	0.5238
0.0634	21.0	126	2.3476	0.5714
0.0488	22.0	132	2.3551	0.5476
0.0488	23.0	138	2.4123	0.5714
0.0014	24.0	144	2.3855	0.5714
0.0006	25.0	150	2.3709	0.5714
0.0006	26.0	156	2.3623	0.5714
0.0004	27.0	162	2.3621	0.5714
0.0004	28.0	168	2.3646	0.5952
0.0003	29.0	174	2.3639	0.5952
0.0003	30.0	180	2.3665	0.5952
0.0003	31.0	186	2.3692	0.5952
0.0002	32.0	192	2.3723	0.5952
0.0002	33.0	198	2.3750	0.5952
0.0002	34.0	204	2.3777	0.5714
0.0002	35.0	210	2.3806	0.5714
0.0002	36.0	216	2.3834	0.5714
0.0002	37.0	222	2.3855	0.5714
0.0002	38.0	228	2.3872	0.5714
0.0001	39.0	234	2.3885	0.5714
0.0001	40.0	240	2.3895	0.5714
0.0001	41.0	246	2.3902	0.5714
0.0001	42.0	252	2.3903	0.5714
0.0001	43.0	258	2.3903	0.5714
0.0001	44.0	264	2.3903	0.5714
0.0001	45.0	270	2.3903	0.5714
0.0001	46.0	276	2.3903	0.5714
0.0001	47.0	282	2.3903	0.5714
0.0001	48.0	288	2.3903	0.5714
0.0001	49.0	294	2.3903	0.5714
0.0001	50.0	300	2.3903	0.5714

Framework versions

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