smids_5x_deit_tiny_rms_0001_fold1

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: 0.9972
• Accuracy: 0.9048

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
0.2949	1.0	376	0.4792	0.7896
0.1877	2.0	752	0.3869	0.8631
0.1943	3.0	1128	0.4273	0.8514
0.1151	4.0	1504	0.4170	0.8932
0.1309	5.0	1880	0.4159	0.8748
0.0937	6.0	2256	0.5222	0.8831
0.0299	7.0	2632	0.5974	0.8932
0.0659	8.0	3008	0.6171	0.8715
0.0586	9.0	3384	0.7200	0.8781
0.0715	10.0	3760	0.9149	0.8664
0.0752	11.0	4136	0.7964	0.8765
0.0401	12.0	4512	0.6968	0.8831
0.0094	13.0	4888	0.6898	0.8865
0.0111	14.0	5264	0.7411	0.8932
0.0334	15.0	5640	0.8411	0.8798
0.0369	16.0	6016	0.7849	0.8798
0.0017	17.0	6392	0.7191	0.8898
0.0026	18.0	6768	0.8047	0.8815
0.0265	19.0	7144	0.6550	0.8982
0.0527	20.0	7520	0.7590	0.8798
0.0052	21.0	7896	0.7860	0.8881
0.001	22.0	8272	0.8487	0.8965
0.0432	23.0	8648	0.8524	0.8865
0.0032	24.0	9024	0.8174	0.9015
0.0001	25.0	9400	0.8214	0.8815
0.0146	26.0	9776	0.9080	0.8765
0.0	27.0	10152	0.8028	0.9032
0.0001	28.0	10528	0.9579	0.8915
0.0043	29.0	10904	0.8349	0.8982
0.0053	30.0	11280	0.9140	0.8831
0.0204	31.0	11656	0.9273	0.8898
0.0001	32.0	12032	0.9480	0.8848
0.0006	33.0	12408	1.0366	0.8865
0.0042	34.0	12784	1.0682	0.8798
0.0025	35.0	13160	0.9542	0.8932
0.0006	36.0	13536	0.8930	0.9048
0.0001	37.0	13912	0.9451	0.8932
0.0112	38.0	14288	1.0303	0.8848
0.0	39.0	14664	1.0298	0.8932
0.0	40.0	15040	0.9996	0.8932
0.0	41.0	15416	0.9909	0.8998
0.0	42.0	15792	0.9652	0.9015
0.0	43.0	16168	0.9547	0.9032
0.0	44.0	16544	0.9994	0.8982
0.0	45.0	16920	0.9802	0.9015
0.003	46.0	17296	0.9911	0.9032
0.0	47.0	17672	0.9936	0.9048
0.0	48.0	18048	0.9937	0.9048
0.0	49.0	18424	0.9932	0.9048
0.0025	50.0	18800	0.9972	0.9048

Framework versions

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