hushem_1x_deit_tiny_sgd_lr001_fold5

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: 1.3032
• Accuracy: 0.3902

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	1.6123	0.1220
1.5785	2.0	12	1.5813	0.1463
1.5785	3.0	18	1.5542	0.1463
1.5395	4.0	24	1.5297	0.1463
1.4749	5.0	30	1.5083	0.1951
1.4749	6.0	36	1.4884	0.1951
1.4296	7.0	42	1.4718	0.1707
1.4296	8.0	48	1.4578	0.1463
1.4059	9.0	54	1.4447	0.1463
1.3876	10.0	60	1.4316	0.2195
1.3876	11.0	66	1.4209	0.2195
1.3523	12.0	72	1.4102	0.2195
1.3523	13.0	78	1.4009	0.2439
1.3412	14.0	84	1.3926	0.2439
1.3216	15.0	90	1.3847	0.2927
1.3216	16.0	96	1.3782	0.3171
1.2923	17.0	102	1.3713	0.3415
1.2923	18.0	108	1.3652	0.3415
1.305	19.0	114	1.3592	0.3415
1.2722	20.0	120	1.3536	0.3415
1.2722	21.0	126	1.3490	0.3659
1.2479	22.0	132	1.3441	0.3659
1.2479	23.0	138	1.3399	0.3659
1.2818	24.0	144	1.3360	0.3659
1.2363	25.0	150	1.3318	0.3659
1.2363	26.0	156	1.3281	0.3659
1.2375	27.0	162	1.3249	0.3659
1.2375	28.0	168	1.3220	0.3659
1.2164	29.0	174	1.3194	0.3659
1.2359	30.0	180	1.3171	0.3902
1.2359	31.0	186	1.3148	0.3902
1.2121	32.0	192	1.3127	0.3902
1.2121	33.0	198	1.3110	0.3902
1.2131	34.0	204	1.3092	0.3902
1.1973	35.0	210	1.3077	0.3902
1.1973	36.0	216	1.3064	0.3902
1.1836	37.0	222	1.3054	0.3902
1.1836	38.0	228	1.3046	0.3902
1.2087	39.0	234	1.3039	0.3902
1.2019	40.0	240	1.3035	0.3902
1.2019	41.0	246	1.3033	0.3902
1.2033	42.0	252	1.3032	0.3902
1.2033	43.0	258	1.3032	0.3902
1.1754	44.0	264	1.3032	0.3902
1.1907	45.0	270	1.3032	0.3902
1.1907	46.0	276	1.3032	0.3902
1.2082	47.0	282	1.3032	0.3902
1.2082	48.0	288	1.3032	0.3902
1.1699	49.0	294	1.3032	0.3902
1.2038	50.0	300	1.3032	0.3902

Framework versions

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