smids_3x_deit_small_adamax_001_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: 0.9284
• Accuracy: 0.8831

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
0.5541	1.0	226	0.6212	0.7379
0.2991	2.0	452	0.3577	0.8748
0.3025	3.0	678	0.3328	0.8765
0.2535	4.0	904	0.3762	0.8631
0.2002	5.0	1130	0.3237	0.8581
0.2266	6.0	1356	0.2994	0.8898
0.1986	7.0	1582	0.4068	0.8831
0.102	8.0	1808	0.5862	0.8564
0.1677	9.0	2034	0.4842	0.8431
0.0849	10.0	2260	0.4657	0.8848
0.0597	11.0	2486	0.4867	0.8698
0.0739	12.0	2712	0.4601	0.8881
0.057	13.0	2938	0.5719	0.8614
0.0476	14.0	3164	0.5741	0.8898
0.0028	15.0	3390	0.5632	0.8932
0.0442	16.0	3616	0.6974	0.8748
0.0232	17.0	3842	0.7080	0.8731
0.0139	18.0	4068	0.6478	0.8848
0.0142	19.0	4294	0.6320	0.8881
0.0278	20.0	4520	0.7359	0.8698
0.0017	21.0	4746	0.5842	0.8948
0.0064	22.0	4972	0.8360	0.8681
0.0257	23.0	5198	0.6566	0.8865
0.0027	24.0	5424	0.6515	0.8948
0.0001	25.0	5650	0.6767	0.8998
0.0009	26.0	5876	0.7783	0.8865
0.0001	27.0	6102	0.7799	0.8815
0.0004	28.0	6328	0.8472	0.8831
0.0002	29.0	6554	0.6998	0.8848
0.0	30.0	6780	0.8253	0.8815
0.0	31.0	7006	0.7884	0.8898
0.0001	32.0	7232	0.8156	0.8798
0.0032	33.0	7458	0.8222	0.8881
0.0	34.0	7684	0.8187	0.8848
0.0	35.0	7910	0.8080	0.8848
0.0	36.0	8136	0.8285	0.8798
0.0	37.0	8362	0.8277	0.8848
0.0	38.0	8588	0.8358	0.8831
0.0	39.0	8814	0.8499	0.8831
0.0	40.0	9040	0.8554	0.8798
0.0034	41.0	9266	0.8762	0.8798
0.0035	42.0	9492	0.8827	0.8798
0.0	43.0	9718	0.8772	0.8798
0.0	44.0	9944	0.8964	0.8831
0.0	45.0	10170	0.9072	0.8831
0.0	46.0	10396	0.9164	0.8815
0.0	47.0	10622	0.9219	0.8815
0.0	48.0	10848	0.9247	0.8831
0.0	49.0	11074	0.9274	0.8831
0.0	50.0	11300	0.9284	0.8831

Framework versions

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