smids_3x_deit_tiny_sgd_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: 1.1190
• Accuracy: 0.405

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
1.3567	1.0	225	1.3548	0.3467
1.2325	2.0	450	1.3329	0.345
1.3433	3.0	675	1.3131	0.3467
1.2405	4.0	900	1.2949	0.3467
1.3135	5.0	1125	1.2785	0.3533
1.2715	6.0	1350	1.2636	0.36
1.2599	7.0	1575	1.2502	0.3583
1.2399	8.0	1800	1.2380	0.365
1.2555	9.0	2025	1.2270	0.3667
1.2073	10.0	2250	1.2172	0.38
1.2054	11.0	2475	1.2082	0.3767
1.2574	12.0	2700	1.2001	0.3817
1.1461	13.0	2925	1.1929	0.385
1.1651	14.0	3150	1.1863	0.3883
1.2048	15.0	3375	1.1803	0.39
1.1932	16.0	3600	1.1750	0.395
1.1333	17.0	3825	1.1701	0.3967
1.1551	18.0	4050	1.1656	0.3917
1.1804	19.0	4275	1.1615	0.3933
1.1749	20.0	4500	1.1578	0.39
1.1596	21.0	4725	1.1543	0.39
1.1144	22.0	4950	1.1511	0.39
1.1024	23.0	5175	1.1481	0.395
1.1547	24.0	5400	1.1454	0.3967
1.1295	25.0	5625	1.1429	0.395
1.1235	26.0	5850	1.1405	0.3983
1.0919	27.0	6075	1.1383	0.3983
1.0892	28.0	6300	1.1364	0.4033
1.1395	29.0	6525	1.1345	0.4017
1.1334	30.0	6750	1.1328	0.4
1.0988	31.0	6975	1.1312	0.405
1.1324	32.0	7200	1.1297	0.4067
1.1023	33.0	7425	1.1284	0.41
1.1357	34.0	7650	1.1271	0.4083
1.1037	35.0	7875	1.1259	0.405
1.1207	36.0	8100	1.1249	0.4033
1.1299	37.0	8325	1.1239	0.4033
1.1551	38.0	8550	1.1231	0.405
1.1268	39.0	8775	1.1223	0.4067
1.1415	40.0	9000	1.1216	0.405
1.1269	41.0	9225	1.1210	0.4067
1.1595	42.0	9450	1.1205	0.4067
1.1387	43.0	9675	1.1201	0.4067
1.0676	44.0	9900	1.1198	0.4067
1.1227	45.0	10125	1.1195	0.4067
1.0985	46.0	10350	1.1193	0.4067
1.0755	47.0	10575	1.1191	0.405
1.0965	48.0	10800	1.1190	0.405
1.1228	49.0	11025	1.1190	0.405
1.0829	50.0	11250	1.1190	0.405

Framework versions

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