dataset_model2

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.5350
• Accuracy: 0.8798

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: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.1141	0.99	62	0.4707	0.8647
0.1098	1.99	124	0.4876	0.8597
0.1444	2.99	186	0.4651	0.8647
0.1088	3.99	248	0.5397	0.8527
0.1404	4.99	310	0.4794	0.8727
0.0656	5.99	372	0.5637	0.8507
0.1126	6.99	434	0.5318	0.8597
0.099	7.99	496	0.5522	0.8597
0.0501	8.99	558	0.5654	0.8667
0.0878	9.99	620	0.5915	0.8517
0.0594	10.99	682	0.5846	0.8717
0.0562	11.99	744	0.5191	0.8778
0.0554	12.99	806	0.5425	0.8717
0.0368	13.99	868	0.5725	0.8778
0.0415	14.99	930	0.5790	0.8637
0.0208	15.99	992	0.5319	0.8788
0.026	16.99	1054	0.5622	0.8677
0.0307	17.99	1116	0.5129	0.8878
0.015	18.99	1178	0.5508	0.8768
0.0263	19.99	1240	0.5350	0.8798

Framework versions

• Transformers 4.26.0.dev0
• Pytorch 1.13.0+cu117
• Datasets 2.7.1
• Tokenizers 0.13.2