hushem_40x_deit_small_sgd_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: 0.3304
• Accuracy: 0.8837

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
1.2372	1.0	217	1.2798	0.3488
1.0621	2.0	434	1.1335	0.5814
0.8881	3.0	651	1.0243	0.5814
0.7868	4.0	868	0.9174	0.6279
0.6948	5.0	1085	0.8587	0.6279
0.5714	6.0	1302	0.7810	0.7209
0.4585	7.0	1519	0.7011	0.8140
0.4277	8.0	1736	0.6580	0.7907
0.3688	9.0	1953	0.6164	0.8140
0.2836	10.0	2170	0.5578	0.8140
0.2148	11.0	2387	0.5322	0.8140
0.2211	12.0	2604	0.5199	0.8140
0.2014	13.0	2821	0.4865	0.8140
0.1799	14.0	3038	0.4815	0.8140
0.1565	15.0	3255	0.4749	0.7907
0.1129	16.0	3472	0.4440	0.8372
0.0992	17.0	3689	0.4542	0.7907
0.1	18.0	3906	0.4290	0.8140
0.0944	19.0	4123	0.4149	0.8140
0.0856	20.0	4340	0.4111	0.8372
0.0816	21.0	4557	0.4115	0.8140
0.0563	22.0	4774	0.3956	0.7907
0.0625	23.0	4991	0.3834	0.7907
0.0683	24.0	5208	0.3893	0.7907
0.0454	25.0	5425	0.3773	0.8140
0.0571	26.0	5642	0.3874	0.7907
0.0322	27.0	5859	0.3743	0.8140
0.0339	28.0	6076	0.3713	0.8372
0.0345	29.0	6293	0.3616	0.8372
0.0434	30.0	6510	0.3686	0.8372
0.0377	31.0	6727	0.3495	0.8605
0.0295	32.0	6944	0.3476	0.8372
0.0279	33.0	7161	0.3534	0.8605
0.0232	34.0	7378	0.3489	0.8372
0.0275	35.0	7595	0.3346	0.8837
0.0214	36.0	7812	0.3309	0.8605
0.018	37.0	8029	0.3342	0.8605
0.0167	38.0	8246	0.3289	0.8837
0.0196	39.0	8463	0.3389	0.8605
0.0269	40.0	8680	0.3388	0.8605
0.0126	41.0	8897	0.3309	0.8605
0.0119	42.0	9114	0.3316	0.8837
0.0174	43.0	9331	0.3268	0.8837
0.0199	44.0	9548	0.3304	0.8837
0.0115	45.0	9765	0.3378	0.8605
0.0138	46.0	9982	0.3301	0.8837
0.0107	47.0	10199	0.3312	0.8605
0.0108	48.0	10416	0.3294	0.9070
0.0125	49.0	10633	0.3301	0.8837
0.0148	50.0	10850	0.3304	0.8837

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2