hushem_40x_deit_tiny_rms_001_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: 7.3853
• Accuracy: 0.5556

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
1.1884	1.0	215	1.1880	0.4444
0.7323	2.0	430	1.1545	0.5111
0.6559	3.0	645	3.0305	0.3556
0.5702	4.0	860	1.2302	0.5111
0.5375	5.0	1075	2.2528	0.4222
0.4472	6.0	1290	1.9208	0.5111
0.4382	7.0	1505	1.8095	0.4889
0.3809	8.0	1720	2.0821	0.4222
0.3012	9.0	1935	1.8136	0.4
0.2478	10.0	2150	2.1397	0.4889
0.2029	11.0	2365	1.9762	0.5556
0.1971	12.0	2580	2.3756	0.5778
0.213	13.0	2795	1.6329	0.6444
0.1476	14.0	3010	2.7699	0.5333
0.098	15.0	3225	2.9763	0.5556
0.1482	16.0	3440	3.4825	0.5111
0.1197	17.0	3655	2.4388	0.6444
0.0935	18.0	3870	2.6931	0.5778
0.0698	19.0	4085	3.8147	0.5333
0.1713	20.0	4300	3.1091	0.5556
0.0331	21.0	4515	3.7485	0.5778
0.0687	22.0	4730	3.9845	0.5778
0.0351	23.0	4945	3.2773	0.6
0.0341	24.0	5160	4.2021	0.5333
0.0324	25.0	5375	6.0388	0.4889
0.022	26.0	5590	4.1761	0.6
0.1048	27.0	5805	2.9470	0.6
0.0202	28.0	6020	3.8209	0.5778
0.0085	29.0	6235	4.1758	0.5111
0.0013	30.0	6450	4.2128	0.5556
0.0026	31.0	6665	4.4304	0.4667
0.0003	32.0	6880	4.6210	0.5111
0.015	33.0	7095	3.7643	0.5556
0.0066	34.0	7310	4.8748	0.5778
0.0	35.0	7525	4.7438	0.5556
0.0	36.0	7740	5.0565	0.5111
0.0	37.0	7955	5.3178	0.5333
0.0	38.0	8170	5.6008	0.5333
0.0	39.0	8385	5.8863	0.5333
0.0	40.0	8600	6.1779	0.5333
0.0	41.0	8815	6.4282	0.5333
0.0	42.0	9030	6.6702	0.5556
0.0	43.0	9245	6.8800	0.5556
0.0	44.0	9460	7.0514	0.5556
0.0	45.0	9675	7.1938	0.5556
0.0	46.0	9890	7.2836	0.5556
0.0	47.0	10105	7.3402	0.5556
0.0	48.0	10320	7.3740	0.5556
0.0	49.0	10535	7.3831	0.5556
0.0	50.0	10750	7.3853	0.5556

Framework versions

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