Entrnal_eyes_data_6_true_agoiment211_model2

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: 0.0883
• Train Accuracy: 0.9406
• Train Top-3-accuracy: 0.9940
• Validation Loss: 0.2930
• Validation Accuracy: 0.9430
• Validation Top-3-accuracy: 0.9943
• 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': 620, '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
1.1642	0.5159	0.8895	0.8054	0.6679	0.9485	0
0.5389	0.7360	0.9637	0.4377	0.7847	0.9737	1
0.3063	0.8169	0.9788	0.3756	0.8425	0.9825	2
0.2024	0.8607	0.9848	0.3307	0.8758	0.9868	3
0.1515	0.8875	0.9882	0.3064	0.8976	0.9893	4
0.1205	0.9058	0.9902	0.2965	0.9127	0.9909	5
0.1071	0.9184	0.9916	0.2962	0.9234	0.9921	6
0.0969	0.9277	0.9926	0.2831	0.9316	0.9930	7
0.0948	0.9348	0.9934	0.2905	0.9379	0.9937	8
0.0883	0.9406	0.9940	0.2930	0.9430	0.9943	9

Framework versions

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