swinv2-tiny-patch4-window8-256-dmae-humeda-DAV17

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: 0.8543
• Accuracy: 0.7885

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: 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: 0.1
• num_epochs: 42

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.9302	10	1.5864	0.2885
6.7245	1.9302	20	1.4466	0.4615
5.7484	2.9302	30	1.0303	0.5962
3.9879	3.9302	40	0.9820	0.5577
2.769	4.9302	50	0.8608	0.6538
2.3766	5.9302	60	0.8945	0.6923
2.3766	6.9302	70	0.7773	0.6346
1.9183	7.9302	80	0.8082	0.6346
1.4993	8.9302	90	0.9407	0.6923
1.3461	9.9302	100	0.9281	0.75
1.2085	10.9302	110	0.7563	0.7692
0.8413	11.9302	120	0.9108	0.7308
0.8413	12.9302	130	0.8543	0.7885
0.9607	13.9302	140	1.2058	0.6731
0.837	14.9302	150	0.9733	0.7115
0.7641	15.9302	160	1.0169	0.6538
0.7997	16.9302	170	0.8486	0.7308
0.6171	17.9302	180	0.9551	0.7885
0.6171	18.9302	190	1.0267	0.7308
0.6755	19.9302	200	1.1810	0.6923
0.6393	20.9302	210	1.0516	0.7308
0.573	21.9302	220	1.1029	0.7115
0.4657	22.9302	230	1.0257	0.7885
0.4626	23.9302	240	1.2266	0.6923
0.4626	24.9302	250	1.3491	0.6538
0.4899	25.9302	260	1.2055	0.7692
0.3991	26.9302	270	1.1633	0.6923
0.3778	27.9302	280	1.1751	0.7308
0.443	28.9302	290	1.1727	0.75
0.43	29.9302	300	1.3292	0.7115
0.43	30.9302	310	1.1873	0.7115
0.4425	31.9302	320	1.2326	0.6538
0.3098	32.9302	330	1.2379	0.7115
0.4086	33.9302	340	1.3020	0.6731
0.3046	34.9302	350	1.2686	0.7115
0.3503	35.9302	360	1.3006	0.6923
0.3503	36.9302	370	1.3207	0.6923
0.2985	37.9302	380	1.3626	0.7115
0.3445	38.9302	390	1.3689	0.7115
0.3017	39.9302	400	1.3523	0.7115
0.3446	40.9302	410	1.3447	0.7115
0.2799	41.9302	420	1.3395	0.7115

Framework versions

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