vit-base-patch16-224-5class224

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

• Train Loss: 0.0115
• Train Accuracy: 0.9460
• Train Top-3-accuracy: 0.9911
• Validation Loss: 0.1621
• Validation Accuracy: 0.9490
• Validation Top-3-accuracy: 0.9916
• Epoch: 6

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:

• optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 574, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
• training_precision: float32

Training results

Train Loss	Train Accuracy	Train Top-3-accuracy	Validation Loss	Validation Accuracy	Validation Top-3-accuracy	Epoch
0.7725	0.6414	0.8898	0.3755	0.7636	0.9478	0
0.2160	0.8219	0.9635	0.2372	0.8557	0.9726	1
0.0696	0.8812	0.9780	0.2035	0.8989	0.9818	2
0.0344	0.9108	0.9842	0.1715	0.9203	0.9860	3
0.0194	0.9278	0.9875	0.1911	0.9337	0.9888	4
0.0147	0.9381	0.9897	0.1651	0.9425	0.9904	5
0.0115	0.9460	0.9911	0.1621	0.9490	0.9916	6

Framework versions

• Transformers 4.41.2
• TensorFlow 2.15.0
• Datasets 2.20.0
• Tokenizers 0.19.1