smids_3x_deit_tiny_sgd_00001_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: 1.0996
• Accuracy: 0.4057

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
1.3881	1.0	226	1.3111	0.3489
1.3128	2.0	452	1.2903	0.3523
1.3013	3.0	678	1.2711	0.3589
1.3217	4.0	904	1.2541	0.3656
1.2917	5.0	1130	1.2386	0.3639
1.3196	6.0	1356	1.2247	0.3656
1.2618	7.0	1582	1.2122	0.3673
1.2868	8.0	1808	1.2013	0.3689
1.2007	9.0	2034	1.1914	0.3790
1.1905	10.0	2260	1.1825	0.3856
1.2678	11.0	2486	1.1746	0.3823
1.1575	12.0	2712	1.1675	0.3856
1.1907	13.0	2938	1.1613	0.3840
1.2093	14.0	3164	1.1556	0.3840
1.2019	15.0	3390	1.1505	0.3756
1.1269	16.0	3616	1.1458	0.3756
1.2046	17.0	3842	1.1416	0.3790
1.1582	18.0	4068	1.1378	0.3740
1.1486	19.0	4294	1.1344	0.3806
1.1865	20.0	4520	1.1312	0.3773
1.1413	21.0	4746	1.1283	0.3773
1.1132	22.0	4972	1.1257	0.3856
1.1589	23.0	5198	1.1233	0.3873
1.1721	24.0	5424	1.1210	0.3856
1.1316	25.0	5650	1.1189	0.3856
1.1482	26.0	5876	1.1170	0.3957
1.126	27.0	6102	1.1153	0.4007
1.0926	28.0	6328	1.1137	0.4040
1.1041	29.0	6554	1.1121	0.4023
1.206	30.0	6780	1.1107	0.3973
1.1379	31.0	7006	1.1094	0.3940
1.1454	32.0	7232	1.1082	0.3990
1.1347	33.0	7458	1.1071	0.4040
1.0924	34.0	7684	1.1061	0.4057
1.0887	35.0	7910	1.1052	0.4057
1.1281	36.0	8136	1.1043	0.4057
1.1197	37.0	8362	1.1035	0.4057
1.0883	38.0	8588	1.1028	0.4090
1.1185	39.0	8814	1.1022	0.4090
1.1206	40.0	9040	1.1017	0.4090
1.1449	41.0	9266	1.1012	0.4073
1.0923	42.0	9492	1.1008	0.4057
1.1262	43.0	9718	1.1004	0.4073
1.1447	44.0	9944	1.1002	0.4040
1.11	45.0	10170	1.0999	0.4040
1.1348	46.0	10396	1.0998	0.4057
1.1134	47.0	10622	1.0997	0.4057
1.119	48.0	10848	1.0996	0.4057
1.1325	49.0	11074	1.0996	0.4057
1.1613	50.0	11300	1.0996	0.4057

Framework versions

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