smids_3x_deit_small_adamax_0001_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: 0.6646
• Accuracy: 0.9115

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: 0.0001
• 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
0.3694	1.0	226	0.3043	0.8765
0.1918	2.0	452	0.2837	0.8948
0.1232	3.0	678	0.5268	0.8464
0.0814	4.0	904	0.3209	0.9048
0.0289	5.0	1130	0.4828	0.8948
0.009	6.0	1356	0.6672	0.8932
0.057	7.0	1582	0.5442	0.9032
0.0008	8.0	1808	0.5498	0.9082
0.037	9.0	2034	0.4929	0.9182
0.0001	10.0	2260	0.5526	0.9082
0.0002	11.0	2486	0.5981	0.9048
0.0001	12.0	2712	0.5439	0.9115
0.0	13.0	2938	0.5693	0.9082
0.017	14.0	3164	0.5385	0.9149
0.003	15.0	3390	0.6151	0.9165
0.0	16.0	3616	0.5918	0.9149
0.0028	17.0	3842	0.5844	0.9115
0.0	18.0	4068	0.5936	0.9149
0.0042	19.0	4294	0.5972	0.9032
0.0	20.0	4520	0.5981	0.9098
0.0	21.0	4746	0.6811	0.9082
0.0168	22.0	4972	0.5972	0.9115
0.0028	23.0	5198	0.6127	0.9132
0.0	24.0	5424	0.6115	0.9149
0.0	25.0	5650	0.6222	0.9149
0.0	26.0	5876	0.6074	0.9215
0.0	27.0	6102	0.6129	0.9165
0.0	28.0	6328	0.6078	0.9165
0.0	29.0	6554	0.6211	0.9215
0.0	30.0	6780	0.6156	0.9149
0.0	31.0	7006	0.6329	0.9149
0.0	32.0	7232	0.6211	0.9215
0.0031	33.0	7458	0.6254	0.9165
0.0	34.0	7684	0.6575	0.9149
0.0	35.0	7910	0.6417	0.9132
0.0	36.0	8136	0.6653	0.9199
0.0	37.0	8362	0.6525	0.9149
0.0	38.0	8588	0.6394	0.9149
0.0	39.0	8814	0.6447	0.9165
0.0	40.0	9040	0.6473	0.9132
0.0027	41.0	9266	0.6511	0.9149
0.0028	42.0	9492	0.6536	0.9149
0.0	43.0	9718	0.6481	0.9149
0.0	44.0	9944	0.6547	0.9132
0.0	45.0	10170	0.6581	0.9132
0.0	46.0	10396	0.6606	0.9132
0.0	47.0	10622	0.6627	0.9132
0.0	48.0	10848	0.6635	0.9132
0.0	49.0	11074	0.6649	0.9115
0.0	50.0	11300	0.6646	0.9115

Framework versions

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