swin-tiny-patch4-window7-224-large-dataset-varicropped

This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 1.3554
• Accuracy: 0.6571

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8523	0.99	88	1.8136	0.3771
0.0725	1.99	176	1.2359	0.6006
0.0397	2.99	264	1.1716	0.6014
0.0179	3.99	352	1.5688	0.5704
0.0173	4.99	440	1.3718	0.6237
0.0097	5.99	528	1.3841	0.5927
0.0109	6.99	616	1.4044	0.5895
0.0019	7.99	704	1.2936	0.6150
0.002	8.99	792	1.4264	0.5760
0.0035	9.99	880	1.2226	0.6396
0.0025	10.99	968	1.1553	0.6635
0.0009	11.99	1056	1.1727	0.6643
0.0037	12.99	1144	1.1182	0.6714
0.0017	13.99	1232	1.4015	0.6364
0.0009	14.99	1320	1.2955	0.6683
0.0002	15.99	1408	1.2310	0.6555
0.0007	16.99	1496	1.3849	0.6325
0.001	17.99	1584	1.4312	0.6102
0.0001	18.99	1672	1.5087	0.6181
0.0002	19.99	1760	1.7247	0.6062
0.0016	20.99	1848	1.5534	0.6237
0.0004	21.99	1936	1.5382	0.6333
0.0008	22.99	2024	1.4910	0.6484
0.0008	23.99	2112	1.5020	0.6380
0.0005	24.99	2200	1.4788	0.6468
0.001	25.99	2288	1.3416	0.6770
0.003	26.99	2376	1.2643	0.6738
0.0001	27.99	2464	1.3582	0.6595
0.0	28.99	2552	1.3767	0.6523
0.0	29.99	2640	1.3554	0.6571

Framework versions

• Transformers 4.21.1
• Pytorch 1.12.1
• Datasets 2.4.0
• Tokenizers 0.12.1