hushem_5x_deit_small_adamax_00001_fold4

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: 0.5172
• Accuracy: 0.8333

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
1.2786	1.0	28	1.2504	0.4286
1.0062	2.0	56	1.1218	0.4524
0.7412	3.0	84	0.9795	0.4762
0.5492	4.0	112	0.8870	0.6190
0.3717	5.0	140	0.7811	0.6667
0.3128	6.0	168	0.7165	0.7619
0.2014	7.0	196	0.6520	0.7143
0.169	8.0	224	0.6139	0.7143
0.0981	9.0	252	0.5693	0.7619
0.0593	10.0	280	0.5620	0.7381
0.0447	11.0	308	0.5082	0.8095
0.0256	12.0	336	0.4626	0.8333
0.0121	13.0	364	0.5351	0.7857
0.0073	14.0	392	0.4890	0.8333
0.0046	15.0	420	0.4933	0.8095
0.004	16.0	448	0.4823	0.8095
0.0033	17.0	476	0.4875	0.8095
0.0029	18.0	504	0.4984	0.8095
0.0023	19.0	532	0.4992	0.8095
0.0021	20.0	560	0.5018	0.8095
0.0018	21.0	588	0.4990	0.8095
0.0017	22.0	616	0.5045	0.8095
0.0014	23.0	644	0.5039	0.8095
0.0014	24.0	672	0.5120	0.8095
0.0013	25.0	700	0.5026	0.8095
0.0011	26.0	728	0.5060	0.8095
0.0012	27.0	756	0.5096	0.8095
0.0011	28.0	784	0.5088	0.8095
0.001	29.0	812	0.5017	0.8095
0.0009	30.0	840	0.5154	0.8095
0.001	31.0	868	0.5070	0.8095
0.0009	32.0	896	0.5093	0.8095
0.0009	33.0	924	0.5133	0.8095
0.0008	34.0	952	0.5115	0.8095
0.0008	35.0	980	0.5134	0.8095
0.0008	36.0	1008	0.5048	0.8333
0.0007	37.0	1036	0.5114	0.8095
0.0007	38.0	1064	0.5110	0.8333
0.0007	39.0	1092	0.5114	0.8333
0.0007	40.0	1120	0.5148	0.8333
0.0007	41.0	1148	0.5122	0.8333
0.0007	42.0	1176	0.5146	0.8333
0.0007	43.0	1204	0.5155	0.8333
0.0007	44.0	1232	0.5189	0.8333
0.0006	45.0	1260	0.5166	0.8333
0.0006	46.0	1288	0.5169	0.8333
0.0007	47.0	1316	0.5173	0.8333
0.0006	48.0	1344	0.5172	0.8333
0.0007	49.0	1372	0.5172	0.8333
0.0007	50.0	1400	0.5172	0.8333

Framework versions

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