swinv2-tiny-patch4-window8-256-dmae-va-U5-42B

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: 0.9637
• Accuracy: 0.6667

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: 4e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 42

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.9	7	7.8663	0.1167
6.936	1.94	15	7.7572	0.1167
6.936	2.97	23	7.1790	0.1167
6.7016	4.0	31	5.9033	0.1167
5.5439	4.9	38	4.6116	0.1167
5.5439	5.94	46	3.2830	0.1167
3.6477	6.97	54	2.2014	0.1167
2.2506	8.0	62	1.5647	0.45
2.2506	8.9	69	1.3160	0.45
1.5088	9.94	77	1.3676	0.3333
1.3868	10.97	85	1.3390	0.45
1.3868	12.0	93	1.3223	0.3833
1.351	12.9	100	1.3156	0.45
1.3271	13.94	108	1.3485	0.4833
1.3271	14.97	116	1.2646	0.4833
1.2322	16.0	124	1.2308	0.4833
1.2322	16.9	131	1.2160	0.5
1.22	17.94	139	1.2015	0.5
1.1899	18.97	147	1.2008	0.5
1.1899	20.0	155	1.1606	0.5
1.109	20.9	162	1.1182	0.5667
1.0603	21.94	170	1.0855	0.5333
1.0603	22.97	178	1.0763	0.5667
1.0264	24.0	186	1.1153	0.5833
1.0086	24.9	193	1.0770	0.65
1.0086	25.94	201	1.0041	0.6167
0.9301	26.97	209	0.9637	0.6667
0.9077	28.0	217	0.9824	0.5833
0.9077	28.9	224	0.9485	0.6
0.8725	29.94	232	0.9294	0.6167
0.8203	30.97	240	0.9348	0.6167
0.8203	32.0	248	0.9295	0.6
0.8211	32.9	255	0.9167	0.6
0.8211	33.94	263	0.9281	0.5833
0.7916	34.97	271	0.8803	0.6333
0.7822	36.0	279	0.8785	0.6333
0.7822	36.9	286	0.8906	0.6
0.7937	37.94	294	0.8899	0.6

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.2+cu118
• Datasets 2.16.1
• Tokenizers 0.15.0