hushem_5x_deit_tiny_adamax_00001_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: 0.5891
• Accuracy: 0.8605

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: 1e-05
• 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
1.3491	1.0	28	1.3171	0.4186
1.0583	2.0	56	1.1404	0.4186
0.8133	3.0	84	1.0626	0.5581
0.7236	4.0	112	0.9689	0.6047
0.5407	5.0	140	0.9154	0.6512
0.4787	6.0	168	0.8329	0.6977
0.4043	7.0	196	0.7849	0.7442
0.3066	8.0	224	0.7047	0.7209
0.2483	9.0	252	0.6601	0.7209
0.1984	10.0	280	0.6346	0.7209
0.1508	11.0	308	0.6148	0.7209
0.1138	12.0	336	0.6034	0.7442
0.0962	13.0	364	0.5398	0.7674
0.0639	14.0	392	0.4866	0.7907
0.0434	15.0	420	0.4751	0.8140
0.0344	16.0	448	0.5249	0.7674
0.0259	17.0	476	0.4934	0.8140
0.0173	18.0	504	0.5157	0.8140
0.0125	19.0	532	0.4794	0.8140
0.0079	20.0	560	0.5000	0.8140
0.0068	21.0	588	0.5083	0.8140
0.0051	22.0	616	0.5005	0.8372
0.0044	23.0	644	0.4949	0.8372
0.0034	24.0	672	0.5221	0.8372
0.003	25.0	700	0.5304	0.8605
0.0025	26.0	728	0.5459	0.8372
0.0023	27.0	756	0.5309	0.8372
0.0022	28.0	784	0.5468	0.8605
0.002	29.0	812	0.5471	0.8372
0.0018	30.0	840	0.5437	0.8372
0.0015	31.0	868	0.5534	0.8372
0.0016	32.0	896	0.5689	0.8605
0.0015	33.0	924	0.5621	0.8605
0.0014	34.0	952	0.5754	0.8605
0.0013	35.0	980	0.5699	0.8605
0.0012	36.0	1008	0.5713	0.8605
0.0013	37.0	1036	0.5830	0.8372
0.0011	38.0	1064	0.5769	0.8372
0.0012	39.0	1092	0.5866	0.8372
0.0011	40.0	1120	0.5802	0.8372
0.0011	41.0	1148	0.5838	0.8605
0.001	42.0	1176	0.5874	0.8605
0.001	43.0	1204	0.5844	0.8605
0.001	44.0	1232	0.5856	0.8605
0.0009	45.0	1260	0.5886	0.8605
0.001	46.0	1288	0.5883	0.8605
0.0009	47.0	1316	0.5899	0.8605
0.0009	48.0	1344	0.5891	0.8605
0.001	49.0	1372	0.5891	0.8605
0.001	50.0	1400	0.5891	0.8605

Framework versions

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