swin-tiny-patch4-window7-224-finetuned-swin-tiny

This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.5222
• Accuracy: 0.5559

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: 0.0001
• 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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.5958	0.96	20	3.5209	0.0937
3.2466	1.98	41	2.9994	0.2387
2.4246	2.99	62	2.0341	0.4169
1.8599	4.0	83	1.6747	0.4955
1.531	4.96	103	1.5218	0.4773
1.3292	5.98	124	1.3834	0.5317
1.2063	6.99	145	1.3381	0.5468
1.0806	8.0	166	1.2748	0.5710
0.9638	8.96	186	1.3062	0.5559
0.8441	9.98	207	1.3322	0.5498
0.7868	10.99	228	1.2873	0.5710
0.7485	12.0	249	1.2012	0.5619
0.6522	12.96	269	1.2264	0.5861
0.6362	13.98	290	1.2796	0.5589
0.6214	14.99	311	1.3406	0.5529
0.5793	16.0	332	1.2479	0.5740
0.5187	16.96	352	1.3203	0.5891
0.4965	17.98	373	1.3429	0.5619
0.4809	18.99	394	1.3453	0.5831
0.4243	20.0	415	1.3759	0.5498
0.4447	20.96	435	1.4275	0.5196
0.3839	21.98	456	1.4660	0.5589
0.414	22.99	477	1.4465	0.5408
0.3741	24.0	498	1.3944	0.5650
0.3802	24.96	518	1.4272	0.5650
0.3733	25.98	539	1.3341	0.5589
0.3558	26.99	560	1.3864	0.5589
0.3448	28.0	581	1.4027	0.5589
0.3373	28.96	601	1.4452	0.5589
0.311	29.98	622	1.4021	0.5740
0.3218	30.99	643	1.4015	0.5680
0.3082	32.0	664	1.4159	0.5619
0.3173	32.96	684	1.4290	0.5498
0.2551	33.98	705	1.4268	0.5619
0.2739	34.99	726	1.4546	0.5559
0.2533	36.0	747	1.4398	0.5498
0.2578	36.96	767	1.4487	0.5438
0.2472	37.98	788	1.4438	0.5559
0.281	38.99	809	1.4916	0.5529
0.2757	40.0	830	1.4758	0.5619
0.2679	40.96	850	1.5104	0.5559
0.2548	41.98	871	1.5024	0.5529
0.2357	42.99	892	1.5286	0.5468
0.2357	44.0	913	1.5150	0.5529
0.2287	44.96	933	1.5234	0.5589
0.2329	45.98	954	1.5334	0.5650
0.2131	46.99	975	1.5296	0.5619
0.2269	48.0	996	1.5221	0.5559
0.2161	48.19	1000	1.5222	0.5559

Framework versions

• Transformers 4.37.0
• Pytorch 2.1.2
• Datasets 2.16.1
• Tokenizers 0.15.1