hushem_1x_deit_small_adamax_001_fold3

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: 3.7699
• Accuracy: 0.4651

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	1.4218	0.2558
1.7221	2.0	12	1.4061	0.3953
1.7221	3.0	18	1.4801	0.3256
1.2972	4.0	24	1.5453	0.3023
1.2115	5.0	30	1.2993	0.3953
1.2115	6.0	36	1.4486	0.3721
1.1196	7.0	42	1.4881	0.3721
1.1196	8.0	48	1.2031	0.4419
1.0394	9.0	54	1.1825	0.4651
0.9076	10.0	60	1.3831	0.3953
0.9076	11.0	66	1.5606	0.3953
0.8351	12.0	72	1.6879	0.3721
0.8351	13.0	78	1.5744	0.5581
0.7325	14.0	84	2.1220	0.5116
0.5767	15.0	90	2.2458	0.4884
0.5767	16.0	96	2.4745	0.3953
0.487	17.0	102	2.9255	0.3953
0.487	18.0	108	2.8169	0.4186
0.265	19.0	114	2.9600	0.4419
0.2739	20.0	120	3.0131	0.3953
0.2739	21.0	126	3.2413	0.4186
0.1684	22.0	132	4.9920	0.3953
0.1684	23.0	138	3.1514	0.5116
0.3265	24.0	144	4.1598	0.3953
0.2652	25.0	150	3.3248	0.4651
0.2652	26.0	156	3.1898	0.4884
0.1992	27.0	162	3.7937	0.3953
0.1992	28.0	168	3.9838	0.4884
0.1826	29.0	174	3.5764	0.3721
0.124	30.0	180	4.1231	0.4419
0.124	31.0	186	4.1455	0.4186
0.1353	32.0	192	3.9925	0.4186
0.1353	33.0	198	3.7016	0.5581
0.0743	34.0	204	3.7997	0.5349
0.0362	35.0	210	3.6073	0.4884
0.0362	36.0	216	3.6198	0.4651
0.0082	37.0	222	3.6509	0.4651
0.0082	38.0	228	3.7081	0.4651
0.003	39.0	234	3.7432	0.4651
0.002	40.0	240	3.7616	0.4651
0.002	41.0	246	3.7690	0.4651
0.0018	42.0	252	3.7699	0.4651
0.0018	43.0	258	3.7699	0.4651
0.0016	44.0	264	3.7699	0.4651
0.0017	45.0	270	3.7699	0.4651
0.0017	46.0	276	3.7699	0.4651
0.0017	47.0	282	3.7699	0.4651
0.0017	48.0	288	3.7699	0.4651
0.0018	49.0	294	3.7699	0.4651
0.0017	50.0	300	3.7699	0.4651

Framework versions

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