smids_10x_deit_tiny_sgd_001_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: 0.3411
• Accuracy: 0.8715

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: 0.001
• 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
0.5089	1.0	751	0.6112	0.7429
0.3967	2.0	1502	0.4763	0.7930
0.2926	3.0	2253	0.3983	0.8314
0.2707	4.0	3004	0.3655	0.8431
0.3017	5.0	3755	0.3388	0.8598
0.2883	6.0	4506	0.3239	0.8581
0.2607	7.0	5257	0.3147	0.8681
0.2345	8.0	6008	0.3166	0.8564
0.2342	9.0	6759	0.3080	0.8765
0.2448	10.0	7510	0.3026	0.8715
0.2489	11.0	8261	0.3046	0.8664
0.2253	12.0	9012	0.3048	0.8731
0.1846	13.0	9763	0.3033	0.8681
0.1475	14.0	10514	0.3005	0.8698
0.1911	15.0	11265	0.2987	0.8731
0.1765	16.0	12016	0.3037	0.8664
0.2371	17.0	12767	0.3023	0.8698
0.1758	18.0	13518	0.3012	0.8731
0.2258	19.0	14269	0.3050	0.8698
0.1902	20.0	15020	0.3050	0.8681
0.1077	21.0	15771	0.3133	0.8698
0.1647	22.0	16522	0.3182	0.8715
0.1389	23.0	17273	0.3149	0.8731
0.1858	24.0	18024	0.3136	0.8664
0.1711	25.0	18775	0.3139	0.8631
0.191	26.0	19526	0.3138	0.8648
0.181	27.0	20277	0.3209	0.8715
0.2185	28.0	21028	0.3215	0.8748
0.1776	29.0	21779	0.3185	0.8715
0.135	30.0	22530	0.3255	0.8681
0.1282	31.0	23281	0.3235	0.8715
0.1784	32.0	24032	0.3244	0.8698
0.2006	33.0	24783	0.3311	0.8581
0.106	34.0	25534	0.3280	0.8715
0.1426	35.0	26285	0.3297	0.8681
0.1309	36.0	27036	0.3304	0.8614
0.2379	37.0	27787	0.3292	0.8748
0.1297	38.0	28538	0.3352	0.8698
0.0635	39.0	29289	0.3365	0.8664
0.1936	40.0	30040	0.3376	0.8698
0.1143	41.0	30791	0.3368	0.8731
0.1251	42.0	31542	0.3385	0.8664
0.1338	43.0	32293	0.3411	0.8681
0.1093	44.0	33044	0.3395	0.8681
0.1208	45.0	33795	0.3395	0.8715
0.1467	46.0	34546	0.3416	0.8698
0.0894	47.0	35297	0.3413	0.8698
0.1318	48.0	36048	0.3412	0.8681
0.0983	49.0	36799	0.3410	0.8715
0.1226	50.0	37550	0.3411	0.8715

Framework versions

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