smids_3x_deit_small_sgd_00001_fold1

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: 1.0200
• Accuracy: 0.4925

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
1.0919	1.0	226	1.0743	0.4407
1.0742	2.0	452	1.0717	0.4391
1.0524	3.0	678	1.0693	0.4357
1.0603	4.0	904	1.0670	0.4391
1.0627	5.0	1130	1.0647	0.4391
1.0778	6.0	1356	1.0625	0.4457
1.0591	7.0	1582	1.0603	0.4441
1.0519	8.0	1808	1.0582	0.4457
1.0538	9.0	2034	1.0562	0.4474
1.0527	10.0	2260	1.0542	0.4491
1.057	11.0	2486	1.0523	0.4491
1.0703	12.0	2712	1.0505	0.4491
1.0101	13.0	2938	1.0488	0.4474
1.0585	14.0	3164	1.0471	0.4491
1.0541	15.0	3390	1.0455	0.4541
1.0416	16.0	3616	1.0438	0.4591
1.03	17.0	3842	1.0423	0.4608
1.0278	18.0	4068	1.0408	0.4624
1.0509	19.0	4294	1.0394	0.4641
1.0188	20.0	4520	1.0381	0.4674
1.0344	21.0	4746	1.0367	0.4674
1.0343	22.0	4972	1.0355	0.4691
1.0324	23.0	5198	1.0343	0.4691
1.0367	24.0	5424	1.0331	0.4725
0.9995	25.0	5650	1.0320	0.4725
1.0409	26.0	5876	1.0310	0.4725
1.0145	27.0	6102	1.0300	0.4758
1.0333	28.0	6328	1.0290	0.4758
1.0283	29.0	6554	1.0281	0.4791
1.0286	30.0	6780	1.0273	0.4808
1.0197	31.0	7006	1.0265	0.4808
1.0259	32.0	7232	1.0257	0.4791
1.0188	33.0	7458	1.0250	0.4808
1.0201	34.0	7684	1.0244	0.4808
1.0339	35.0	7910	1.0238	0.4825
1.0181	36.0	8136	1.0232	0.4875
1.0181	37.0	8362	1.0227	0.4875
1.0115	38.0	8588	1.0223	0.4875
0.9878	39.0	8814	1.0218	0.4891
1.0096	40.0	9040	1.0215	0.4891
1.024	41.0	9266	1.0212	0.4891
1.0376	42.0	9492	1.0209	0.4908
1.0233	43.0	9718	1.0206	0.4925
1.0195	44.0	9944	1.0204	0.4925
1.0135	45.0	10170	1.0203	0.4925
1.039	46.0	10396	1.0202	0.4925
1.0136	47.0	10622	1.0201	0.4925
1.0247	48.0	10848	1.0200	0.4925
1.0178	49.0	11074	1.0200	0.4925
1.0351	50.0	11300	1.0200	0.4925

Framework versions

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