swinv2-tiny-patch4-window8-256-dmae-humeda-DAV3

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: 0.2067
• Accuracy: 0.9589

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: 3e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• 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: linear
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.9845	1.0	21	1.6832	0.3425
2.4369	2.0	42	1.1981	0.4384
1.7752	3.0	63	0.8412	0.6301
1.3772	4.0	84	0.7895	0.7123
1.1556	5.0	105	0.7385	0.7808
1.0059	6.0	126	0.6626	0.8082
0.8598	7.0	147	0.5403	0.7808
0.8724	8.0	168	0.5520	0.8219
0.7096	9.0	189	0.5182	0.8356
0.5038	10.0	210	0.4133	0.8493
0.4951	11.0	231	0.3548	0.8767
0.4692	12.0	252	0.3845	0.8493
0.5339	13.0	273	0.3178	0.8904
0.4536	14.0	294	0.3252	0.8904
0.4369	15.0	315	0.2785	0.8904
0.3941	16.0	336	0.2900	0.9041
0.4363	17.0	357	0.3426	0.8630
0.2819	18.0	378	0.2839	0.9041
0.361	19.0	399	0.2223	0.9041
0.1857	20.0	420	0.2522	0.9178
0.3161	21.0	441	0.2164	0.9178
0.3273	22.0	462	0.2224	0.9315
0.3458	23.0	483	0.2199	0.9452
0.337	24.0	504	0.2377	0.9315
0.1801	25.0	525	0.2067	0.9589
0.3283	26.0	546	0.2401	0.9315
0.2211	27.0	567	0.2167	0.9315
0.1783	28.0	588	0.2180	0.9315
0.2783	28.5854	600	0.2223	0.9315

Framework versions

• Transformers 4.47.1
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0