hushem_5x_deit_small_rms_001_fold5

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: 1.2275
• Accuracy: 0.6585

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
1.7524	1.0	28	1.5298	0.2439
1.4312	2.0	56	1.4291	0.2683
1.3924	3.0	84	1.4059	0.2927
1.4173	4.0	112	1.3938	0.2683
1.3939	5.0	140	1.3790	0.2683
1.3863	6.0	168	1.4164	0.2439
1.3865	7.0	196	1.3790	0.2683
1.394	8.0	224	1.3790	0.2683
1.3883	9.0	252	1.4097	0.2683
1.3472	10.0	280	1.2478	0.4390
1.3905	11.0	308	1.2068	0.3902
1.1031	12.0	336	1.2038	0.4390
1.1503	13.0	364	1.0846	0.4634
1.2064	14.0	392	1.1395	0.4146
1.1249	15.0	420	1.1544	0.4146
1.1285	16.0	448	1.0714	0.4634
1.1149	17.0	476	0.9771	0.6098
1.0493	18.0	504	0.9974	0.4634
0.9938	19.0	532	0.9792	0.5366
1.0212	20.0	560	0.9949	0.5854
0.9943	21.0	588	1.0078	0.5366
1.0044	22.0	616	0.9007	0.5366
1.0661	23.0	644	1.2742	0.4878
0.9523	24.0	672	0.9851	0.6829
0.8733	25.0	700	0.9430	0.5854
0.8075	26.0	728	0.9660	0.6585
0.9128	27.0	756	0.9161	0.7561
0.8898	28.0	784	0.8767	0.7073
0.8051	29.0	812	0.8174	0.6829
0.8328	30.0	840	0.8077	0.6585
0.81	31.0	868	0.7911	0.6585
0.7372	32.0	896	1.0262	0.6585
0.7641	33.0	924	1.0698	0.5854
0.7745	34.0	952	0.8530	0.6829
0.7037	35.0	980	1.0106	0.6585
0.7449	36.0	1008	0.8975	0.7073
0.7391	37.0	1036	0.9607	0.6829
0.7447	38.0	1064	1.0096	0.6585
0.7043	39.0	1092	1.0986	0.7073
0.6379	40.0	1120	1.0787	0.6829
0.6476	41.0	1148	1.0057	0.6829
0.5799	42.0	1176	1.1714	0.6341
0.5954	43.0	1204	1.1356	0.6829
0.6189	44.0	1232	1.1609	0.6829
0.5672	45.0	1260	1.1726	0.6829
0.5115	46.0	1288	1.2388	0.6829
0.4522	47.0	1316	1.2273	0.6829
0.4728	48.0	1344	1.2290	0.6585
0.4195	49.0	1372	1.2275	0.6585
0.4871	50.0	1400	1.2275	0.6585

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0