hushem_40x_deit_small_sgd_0001_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: 1.1108
• Accuracy: 0.5238

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
1.763	1.0	219	1.5785	0.2857
1.5719	2.0	438	1.5003	0.2857
1.452	3.0	657	1.4620	0.2143
1.4006	4.0	876	1.4368	0.2143
1.3854	5.0	1095	1.4164	0.2143
1.3041	6.0	1314	1.3988	0.2381
1.296	7.0	1533	1.3830	0.2619
1.276	8.0	1752	1.3685	0.2381
1.2474	9.0	1971	1.3546	0.2381
1.2128	10.0	2190	1.3420	0.2381
1.2113	11.0	2409	1.3297	0.2381
1.2121	12.0	2628	1.3176	0.2619
1.1861	13.0	2847	1.3062	0.2619
1.1756	14.0	3066	1.2946	0.3095
1.1431	15.0	3285	1.2837	0.3571
1.1487	16.0	3504	1.2730	0.3095
1.1705	17.0	3723	1.2625	0.3095
1.1482	18.0	3942	1.2522	0.2857
1.1037	19.0	4161	1.2421	0.3095
1.0872	20.0	4380	1.2325	0.3810
1.1026	21.0	4599	1.2229	0.4048
1.0517	22.0	4818	1.2135	0.4048
1.0226	23.0	5037	1.2052	0.4286
1.0485	24.0	5256	1.1974	0.4286
1.0319	25.0	5475	1.1896	0.4286
0.9983	26.0	5694	1.1821	0.4286
1.0014	27.0	5913	1.1755	0.4048
1.0162	28.0	6132	1.1694	0.4048
0.986	29.0	6351	1.1635	0.4048
0.9747	30.0	6570	1.1582	0.4286
0.9811	31.0	6789	1.1532	0.4286
0.9907	32.0	7008	1.1482	0.4286
0.9904	33.0	7227	1.1437	0.4286
0.9293	34.0	7446	1.1399	0.4524
0.9752	35.0	7665	1.1362	0.4524
0.9789	36.0	7884	1.1326	0.4762
0.9516	37.0	8103	1.1293	0.5
0.9703	38.0	8322	1.1262	0.5
0.8944	39.0	8541	1.1236	0.5238
0.9388	40.0	8760	1.1213	0.5238
0.9573	41.0	8979	1.1191	0.5238
0.9441	42.0	9198	1.1172	0.5238
0.9438	43.0	9417	1.1156	0.5238
0.9221	44.0	9636	1.1141	0.5238
0.9079	45.0	9855	1.1130	0.5238
0.962	46.0	10074	1.1121	0.5238
0.9464	47.0	10293	1.1114	0.5238
0.9323	48.0	10512	1.1110	0.5238
0.9581	49.0	10731	1.1108	0.5238
0.942	50.0	10950	1.1108	0.5238

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2