swinv2-tiny-patch4-window8-256-dmae-humeda-DAV4

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: 0.9551
• Accuracy: 0.7115

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
3.1112	1.0	23	1.4616	0.4423
2.4301	2.0	46	1.3378	0.3846
1.8107	3.0	69	1.1497	0.4423
1.3272	4.0	92	1.2177	0.5
1.2039	5.0	115	1.1250	0.5577
1.0311	6.0	138	1.0660	0.5577
1.0515	7.0	161	1.2242	0.5
0.8709	8.0	184	1.0952	0.5962
0.677	9.0	207	1.1033	0.5385
0.6763	10.0	230	0.9551	0.7115
0.5749	11.0	253	1.0428	0.6346
0.4896	12.0	276	1.0981	0.6538
0.4817	13.0	299	1.3429	0.4808
0.4264	14.0	322	1.3040	0.6154
0.5637	15.0	345	1.2592	0.4808
0.3846	16.0	368	1.1849	0.6154
0.5337	17.0	391	1.2025	0.6346
0.34	18.0	414	1.0894	0.6346
0.3511	19.0	437	1.2145	0.6346
0.2539	20.0	460	1.1755	0.6346
0.2683	21.0	483	1.2359	0.6731
0.3144	22.0	506	1.2633	0.6538
0.3249	23.0	529	1.2980	0.6346
0.2363	24.0	552	1.1872	0.6538
0.2876	25.0	575	1.2377	0.6923
0.2694	26.0	598	1.2695	0.6538
0.2307	27.0	621	1.2481	0.6731
0.2508	28.0	644	1.3112	0.6731
0.3558	29.0	667	1.3209	0.6731
0.2418	30.0	690	1.3233	0.6538

Framework versions

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