swinv2-base-patch4-window8-256-dmae-humeda-DAV15

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

• Loss: 0.8423
• Accuracy: 0.75

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: 2e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• 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: 42

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.8696	5	1.5972	0.3077
6.7562	1.8696	10	1.5357	0.3077
6.7562	2.8696	15	1.4954	0.4038
6.2842	3.8696	20	1.4612	0.3462
6.2842	4.8696	25	1.3875	0.3269
4.9858	5.8696	30	1.3370	0.3462
4.9858	6.8696	35	1.2739	0.4423
3.5596	7.8696	40	1.1774	0.4808
3.5596	8.8696	45	1.1214	0.4808
2.6814	9.8696	50	1.0999	0.5192
2.6814	10.8696	55	1.1773	0.4615
2.3236	11.8696	60	0.9874	0.5192
2.3236	12.8696	65	1.1124	0.5
1.8037	13.8696	70	0.8936	0.6538
1.8037	14.8696	75	1.2064	0.4423
1.6474	15.8696	80	0.8423	0.75
1.6474	16.8696	85	1.0134	0.6346
1.5505	17.8696	90	0.8965	0.6923
1.5505	18.8696	95	0.9215	0.6538
1.2697	19.8696	100	1.0155	0.6154
1.2697	20.8696	105	0.8500	0.7115
1.1783	21.8696	110	0.9573	0.6538
1.1783	22.8696	115	0.8915	0.6923
1.0235	23.8696	120	0.9831	0.6538
1.0235	24.8696	125	0.9464	0.6538
0.9706	25.8696	130	0.9413	0.6923
0.9706	26.8696	135	1.0249	0.6346
0.9409	27.8696	140	0.9754	0.6538
0.9409	28.8696	145	0.9530	0.7115
0.9447	29.8696	150	1.0266	0.6538
0.9447	30.8696	155	1.0819	0.6538
0.8352	31.8696	160	0.9922	0.6923
0.8352	32.8696	165	0.9755	0.6923
0.8055	33.8696	170	0.9768	0.7115
0.8055	34.8696	175	0.9950	0.6923
0.7481	35.8696	180	1.0135	0.6923
0.7481	36.8696	185	1.0168	0.6923
0.7483	37.8696	190	1.0091	0.6923
0.7483	38.8696	195	1.0055	0.6923
0.8145	39.8696	200	1.0040	0.6923
0.8145	40.8696	205	1.0039	0.6923
0.7501	41.8696	210	1.0038	0.6923

Framework versions

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