Entrnal_eyes_data_7class_allNew_withother_resize_224_model

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

• Train Loss: 0.0693
• Train Accuracy: 0.9107
• Train Top-3-accuracy: 0.9914
• Validation Loss: 0.4731
• Validation Accuracy: 0.9137
• Validation Top-3-accuracy: 0.9918
• Epoch: 9

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': 1580, '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
1.1195	0.5630	0.8481	0.7181	0.7020	0.9377	0
0.5314	0.7457	0.9559	0.5566	0.7758	0.9668	1
0.3817	0.7982	0.9725	0.4695	0.8146	0.9767	2
0.2853	0.8284	0.9795	0.4379	0.8405	0.9819	3
0.2111	0.8515	0.9837	0.4234	0.8605	0.9852	4
0.1475	0.8695	0.9864	0.4329	0.8767	0.9874	5
0.1070	0.8835	0.9882	0.4625	0.8896	0.9890	6
0.0847	0.8948	0.9896	0.4766	0.8993	0.9901	7
0.0745	0.9035	0.9906	0.4688	0.9073	0.9910	8
0.0693	0.9107	0.9914	0.4731	0.9137	0.9918	9

Framework versions

• Transformers 4.44.2
• TensorFlow 2.15.1
• Datasets 3.0.0
• Tokenizers 0.19.1