smids_3x_deit_tiny_adamax_0001_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: 0.8020
• Accuracy: 0.8982

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
0.3976	1.0	226	0.3057	0.8848
0.2433	2.0	452	0.3108	0.8698
0.1348	3.0	678	0.3373	0.8982
0.1034	4.0	904	0.3559	0.9032
0.1083	5.0	1130	0.4835	0.8648
0.066	6.0	1356	0.5085	0.8881
0.0304	7.0	1582	0.5167	0.8982
0.0203	8.0	1808	0.4920	0.8915
0.0411	9.0	2034	0.6349	0.8948
0.0001	10.0	2260	0.5499	0.9015
0.0005	11.0	2486	0.8482	0.8715
0.0006	12.0	2712	0.6367	0.9082
0.0001	13.0	2938	0.6540	0.9082
0.0145	14.0	3164	0.7445	0.8848
0.0002	15.0	3390	0.7327	0.8948
0.0002	16.0	3616	0.7425	0.8898
0.0076	17.0	3842	0.8300	0.8881
0.0	18.0	4068	0.8281	0.8898
0.0117	19.0	4294	0.8348	0.8865
0.0	20.0	4520	0.7762	0.8932
0.0101	21.0	4746	0.8545	0.8781
0.0102	22.0	4972	0.6911	0.9048
0.0118	23.0	5198	0.7788	0.8965
0.0134	24.0	5424	0.8281	0.8898
0.0	25.0	5650	0.8580	0.8865
0.0002	26.0	5876	0.8155	0.8965
0.0	27.0	6102	0.7810	0.8982
0.0	28.0	6328	0.7738	0.9048
0.0	29.0	6554	0.7899	0.8915
0.0	30.0	6780	0.7800	0.9015
0.0	31.0	7006	0.7916	0.8982
0.0	32.0	7232	0.7756	0.8965
0.0037	33.0	7458	0.7754	0.9015
0.0	34.0	7684	0.7805	0.8982
0.0	35.0	7910	0.7846	0.8932
0.0	36.0	8136	0.8045	0.8932
0.0	37.0	8362	0.7958	0.8948
0.0	38.0	8588	0.7780	0.8965
0.0	39.0	8814	0.7869	0.8998
0.0	40.0	9040	0.7801	0.8965
0.0031	41.0	9266	0.7806	0.9032
0.0032	42.0	9492	0.8019	0.8982
0.0	43.0	9718	0.7990	0.8982
0.0	44.0	9944	0.8009	0.8965
0.0	45.0	10170	0.8036	0.8982
0.0	46.0	10396	0.8034	0.8982
0.0	47.0	10622	0.8034	0.8982
0.0	48.0	10848	0.8001	0.8982
0.0	49.0	11074	0.8018	0.8982
0.0	50.0	11300	0.8020	0.8982

Framework versions

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