smids_3x_deit_tiny_sgd_00001_fold2

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: 1.1011
• Accuracy: 0.4027

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
1.2975	1.0	225	1.3313	0.3461
1.3535	2.0	450	1.3098	0.3411
1.364	3.0	675	1.2899	0.3411
1.2928	4.0	900	1.2721	0.3411
1.2627	5.0	1125	1.2559	0.3378
1.2073	6.0	1350	1.2414	0.3461
1.3184	7.0	1575	1.2280	0.3527
1.2058	8.0	1800	1.2162	0.3527
1.2305	9.0	2025	1.2057	0.3511
1.2453	10.0	2250	1.1960	0.3478
1.1822	11.0	2475	1.1875	0.3461
1.1856	12.0	2700	1.1797	0.3561
1.1979	13.0	2925	1.1728	0.3661
1.1589	14.0	3150	1.1665	0.3644
1.1625	15.0	3375	1.1608	0.3677
1.1751	16.0	3600	1.1557	0.3744
1.1846	17.0	3825	1.1510	0.3760
1.1541	18.0	4050	1.1466	0.3744
1.1807	19.0	4275	1.1426	0.3727
1.1744	20.0	4500	1.1389	0.3710
1.1694	21.0	4725	1.1356	0.3710
1.1819	22.0	4950	1.1325	0.3727
1.1574	23.0	5175	1.1297	0.3794
1.159	24.0	5400	1.1270	0.3760
1.1656	25.0	5625	1.1246	0.3760
1.1491	26.0	5850	1.1224	0.3777
1.1877	27.0	6075	1.1202	0.3760
1.1245	28.0	6300	1.1183	0.3810
1.1465	29.0	6525	1.1164	0.3877
1.0989	30.0	6750	1.1147	0.3910
1.1019	31.0	6975	1.1132	0.3927
1.1115	32.0	7200	1.1117	0.3927
1.1193	33.0	7425	1.1103	0.3943
1.1111	34.0	7650	1.1091	0.3960
1.1163	35.0	7875	1.1080	0.3977
1.1433	36.0	8100	1.1069	0.3993
1.0817	37.0	8325	1.1060	0.3993
1.1389	38.0	8550	1.1052	0.3993
1.1196	39.0	8775	1.1044	0.4027
1.1051	40.0	9000	1.1037	0.4043
1.1003	41.0	9225	1.1031	0.4027
1.1259	42.0	9450	1.1026	0.4027
1.1127	43.0	9675	1.1022	0.4027
1.1252	44.0	9900	1.1018	0.4010
1.0665	45.0	10125	1.1016	0.4027
1.1219	46.0	10350	1.1014	0.4027
1.1281	47.0	10575	1.1012	0.4027
1.0847	48.0	10800	1.1011	0.4027
1.1349	49.0	11025	1.1011	0.4027
1.1316	50.0	11250	1.1011	0.4027

Framework versions

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