swin-tiny-patch4-window7-224-finetuned-omars6

This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.5625
• Accuracy: 0.8815

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: 0.0005
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.9598	0.99	92	0.7744	0.6869
0.7825	2.0	185	0.7336	0.7082
0.9638	2.99	277	0.8202	0.7204
1.0288	4.0	370	0.8621	0.7903
0.9711	4.99	462	0.8212	0.6809
1.0125	6.0	555	0.8700	0.7356
0.945	6.99	647	0.7959	0.7781
0.9851	8.0	740	0.8755	0.6140
0.8078	8.99	832	0.6970	0.7781
0.7377	10.0	925	0.6063	0.7386
0.7934	10.99	1017	0.6121	0.8116
0.7986	12.0	1110	0.6532	0.8116
0.6129	12.99	1202	0.7250	0.8450
0.7428	14.0	1295	0.6417	0.7264
0.5661	14.99	1387	0.6847	0.7964
0.6631	16.0	1480	0.5470	0.8298
0.5787	16.99	1572	0.5696	0.8359
0.6635	18.0	1665	0.6385	0.7872
0.5251	18.99	1757	0.5842	0.8419
0.6164	20.0	1850	0.5506	0.8207
0.4166	20.99	1942	0.8169	0.8055
0.4189	22.0	2035	0.5882	0.8480
0.699	22.99	2127	0.5767	0.8541
0.6095	24.0	2220	0.6392	0.8845
0.3837	24.99	2312	0.6109	0.8723
0.4916	26.0	2405	0.4862	0.8754
0.4536	26.99	2497	0.5625	0.8754
0.3636	28.0	2590	0.5948	0.8663
0.4004	28.99	2682	0.5735	0.8906
0.4248	29.84	2760	0.5625	0.8815

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.1+cu117
• Datasets 2.13.0
• Tokenizers 0.13.3