smids_1x_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: 1.0403
• Accuracy: 0.4775

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
1.0855	1.0	76	1.0750	0.4424
1.0478	2.0	152	1.0731	0.4391
1.0766	3.0	228	1.0714	0.4391
1.0629	4.0	304	1.0697	0.4357
1.094	5.0	380	1.0680	0.4341
1.0477	6.0	456	1.0665	0.4391
1.0623	7.0	532	1.0650	0.4441
1.0732	8.0	608	1.0636	0.4457
1.0667	9.0	684	1.0622	0.4491
1.0551	10.0	760	1.0610	0.4491
1.0356	11.0	836	1.0597	0.4491
1.0548	12.0	912	1.0586	0.4508
1.0615	13.0	988	1.0574	0.4591
1.0286	14.0	1064	1.0564	0.4574
1.0112	15.0	1140	1.0553	0.4558
1.0377	16.0	1216	1.0543	0.4574
1.0367	17.0	1292	1.0534	0.4608
1.0315	18.0	1368	1.0524	0.4608
1.0307	19.0	1444	1.0516	0.4608
1.0759	20.0	1520	1.0507	0.4658
1.0618	21.0	1596	1.0499	0.4674
1.0607	22.0	1672	1.0492	0.4691
1.0436	23.0	1748	1.0485	0.4741
1.0498	24.0	1824	1.0478	0.4758
1.0615	25.0	1900	1.0471	0.4741
1.0688	26.0	1976	1.0465	0.4741
1.0336	27.0	2052	1.0459	0.4741
1.0515	28.0	2128	1.0454	0.4741
1.042	29.0	2204	1.0449	0.4775
1.0112	30.0	2280	1.0444	0.4791
1.0359	31.0	2356	1.0439	0.4791
1.0424	32.0	2432	1.0435	0.4791
1.0133	33.0	2508	1.0431	0.4808
1.0411	34.0	2584	1.0427	0.4791
1.0158	35.0	2660	1.0424	0.4775
1.0378	36.0	2736	1.0421	0.4775
1.0599	37.0	2812	1.0418	0.4791
1.0074	38.0	2888	1.0416	0.4775
1.0149	39.0	2964	1.0413	0.4775
1.0618	40.0	3040	1.0411	0.4775
1.0242	41.0	3116	1.0409	0.4775
1.0268	42.0	3192	1.0408	0.4775
1.0441	43.0	3268	1.0407	0.4758
1.0356	44.0	3344	1.0406	0.4775
1.0273	45.0	3420	1.0405	0.4775
1.0473	46.0	3496	1.0404	0.4775
1.0225	47.0	3572	1.0404	0.4775
1.0411	48.0	3648	1.0404	0.4775
1.0464	49.0	3724	1.0403	0.4775
1.0229	50.0	3800	1.0403	0.4775

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0