smids_3x_deit_small_adamax_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: 0.8130
• Accuracy: 0.8815

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
0.424	1.0	226	0.3513	0.8731
0.2946	2.0	452	0.3000	0.8798
0.1827	3.0	678	0.2979	0.8865
0.2004	4.0	904	0.3148	0.8932
0.1798	5.0	1130	0.3139	0.8998
0.0853	6.0	1356	0.3299	0.8965
0.0515	7.0	1582	0.3457	0.8865
0.0826	8.0	1808	0.3663	0.8932
0.0216	9.0	2034	0.3989	0.8948
0.0193	10.0	2260	0.4487	0.8948
0.0094	11.0	2486	0.5419	0.8815
0.0096	12.0	2712	0.5555	0.8898
0.0006	13.0	2938	0.5917	0.8881
0.0105	14.0	3164	0.6078	0.8982
0.0003	15.0	3390	0.6602	0.8815
0.0002	16.0	3616	0.6567	0.8815
0.0159	17.0	3842	0.6787	0.8881
0.0001	18.0	4068	0.7011	0.8865
0.0111	19.0	4294	0.7444	0.8815
0.0001	20.0	4520	0.7129	0.8865
0.0001	21.0	4746	0.7374	0.8898
0.0255	22.0	4972	0.7516	0.8748
0.0198	23.0	5198	0.7561	0.8898
0.0001	24.0	5424	0.7448	0.8865
0.0	25.0	5650	0.7702	0.8831
0.0001	26.0	5876	0.7830	0.8798
0.0	27.0	6102	0.7463	0.8898
0.0001	28.0	6328	0.7479	0.8815
0.0	29.0	6554	0.7622	0.8898
0.0	30.0	6780	0.7645	0.8898
0.0	31.0	7006	0.7711	0.8898
0.0	32.0	7232	0.7799	0.8865
0.012	33.0	7458	0.7796	0.8898
0.0	34.0	7684	0.8089	0.8848
0.0	35.0	7910	0.7888	0.8831
0.0	36.0	8136	0.8152	0.8815
0.0	37.0	8362	0.7978	0.8831
0.0	38.0	8588	0.7987	0.8781
0.0	39.0	8814	0.7888	0.8915
0.0	40.0	9040	0.7970	0.8831
0.0095	41.0	9266	0.8023	0.8815
0.0008	42.0	9492	0.8065	0.8798
0.0	43.0	9718	0.7964	0.8881
0.0	44.0	9944	0.8002	0.8865
0.0	45.0	10170	0.8035	0.8831
0.0	46.0	10396	0.8056	0.8815
0.0	47.0	10622	0.8110	0.8815
0.0	48.0	10848	0.8125	0.8831
0.0	49.0	11074	0.8131	0.8815
0.0	50.0	11300	0.8130	0.8815

Framework versions

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