hushem_40x_deit_tiny_sgd_0001_fold3

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: 0.9959
• Accuracy: 0.6744

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
1.4187	1.0	217	1.4585	0.3023
1.3875	2.0	434	1.4331	0.2791
1.3134	3.0	651	1.4120	0.3256
1.3061	4.0	868	1.3930	0.3488
1.3015	5.0	1085	1.3762	0.3721
1.2507	6.0	1302	1.3597	0.3721
1.2542	7.0	1519	1.3427	0.3721
1.2153	8.0	1736	1.3276	0.3721
1.2187	9.0	1953	1.3127	0.4186
1.1894	10.0	2170	1.2981	0.4186
1.1545	11.0	2387	1.2843	0.4186
1.1296	12.0	2604	1.2697	0.4419
1.1425	13.0	2821	1.2546	0.4651
1.1006	14.0	3038	1.2420	0.4884
1.101	15.0	3255	1.2295	0.4884
1.0751	16.0	3472	1.2159	0.4884
1.0907	17.0	3689	1.2031	0.4884
1.047	18.0	3906	1.1903	0.5116
1.0396	19.0	4123	1.1781	0.5581
1.0151	20.0	4340	1.1663	0.5581
1.0071	21.0	4557	1.1547	0.5581
0.9605	22.0	4774	1.1441	0.5814
0.9825	23.0	4991	1.1328	0.6047
0.9877	24.0	5208	1.1238	0.6047
0.944	25.0	5425	1.1139	0.6047
1.0028	26.0	5642	1.1046	0.6047
0.9583	27.0	5859	1.0948	0.6279
0.9319	28.0	6076	1.0861	0.6279
0.8861	29.0	6293	1.0779	0.6279
0.9631	30.0	6510	1.0704	0.6512
0.8801	31.0	6727	1.0625	0.6512
0.9404	32.0	6944	1.0548	0.6512
0.9252	33.0	7161	1.0485	0.6512
0.8258	34.0	7378	1.0422	0.6512
0.8739	35.0	7595	1.0361	0.6744
0.8975	36.0	7812	1.0306	0.6744
0.8371	37.0	8029	1.0260	0.6744
0.8695	38.0	8246	1.0212	0.6744
0.8346	39.0	8463	1.0171	0.6744
0.8685	40.0	8680	1.0135	0.6744
0.8448	41.0	8897	1.0098	0.6744
0.8514	42.0	9114	1.0067	0.6744
0.8326	43.0	9331	1.0041	0.6744
0.8323	44.0	9548	1.0018	0.6744
0.8178	45.0	9765	0.9998	0.6744
0.8479	46.0	9982	0.9982	0.6744
0.8512	47.0	10199	0.9971	0.6744
0.851	48.0	10416	0.9963	0.6744
0.839	49.0	10633	0.9959	0.6744
0.7968	50.0	10850	0.9959	0.6744

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.1+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2