vit-base-patch16-224-4class224

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: 0.0136
• Train Accuracy: 0.9421
• Train Top-3-accuracy: 0.9958
• Validation Loss: 0.1390
• Validation Accuracy: 0.9458
• Validation Top-3-accuracy: 0.9961
• 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': 455, '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
0.7231	0.5836	0.9174	0.3551	0.7352	0.9701	0
0.2208	0.8012	0.9802	0.2265	0.8400	0.9858	1
0.0854	0.8664	0.9886	0.1859	0.8862	0.9907	2
0.0372	0.8996	0.9920	0.1565	0.9111	0.9931	3
0.0212	0.9199	0.9938	0.1411	0.9272	0.9945	4
0.0167	0.9328	0.9950	0.1374	0.9379	0.9954	5
0.0136	0.9421	0.9958	0.1390	0.9458	0.9961	6

Framework versions

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