swinv2transformer-brats-artifact-classifier

This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.6292
• Accuracy: 0.9480
• Precision: 0.9536
• Recall: 0.9480
• F1: 0.9483

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
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	80	0.5925	0.7626	0.8094	0.7626	0.7546
0.6976	2.0	160	0.2719	0.8975	0.9048	0.8975	0.8989
0.6976	3.0	240	0.2157	0.9045	0.9159	0.9045	0.9044
0.138	4.0	320	0.3328	0.8919	0.9125	0.8919	0.8944
0.138	5.0	400	0.3456	0.8947	0.9052	0.8947	0.8927
0.0775	6.0	480	0.2108	0.9382	0.9405	0.9382	0.9381
0.0775	7.0	560	0.2550	0.9452	0.9493	0.9452	0.9455
0.0464	8.0	640	0.3084	0.9270	0.9314	0.9270	0.9270
0.0464	9.0	720	0.3198	0.9537	0.9539	0.9537	0.9536
0.0169	10.0	800	0.2893	0.9494	0.9531	0.9494	0.9492
0.0169	11.0	880	0.5885	0.9424	0.9461	0.9424	0.9427
0.0055	12.0	960	0.6292	0.9480	0.9536	0.9480	0.9483

Framework versions

• Transformers 4.53.3
• Pytorch 2.6.0+cu124
• Datasets 4.4.1
• Tokenizers 0.21.2