Imene/vit-base-patch16-224-in21k-Wr

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: 0.3104
• Train Accuracy: 0.9956
• Train Top-3-accuracy: 0.9981
• Validation Loss: 1.6041
• Validation Accuracy: 0.5770
• Validation Top-3-accuracy: 0.8035
• Epoch: 7

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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 1500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
• training_precision: mixed_float16

Training results

Train Loss	Train Accuracy	Train Top-3-accuracy	Validation Loss	Validation Accuracy	Validation Top-3-accuracy	Epoch
3.8300	0.0583	0.1381	3.6801	0.0951	0.2203	0
3.2915	0.2418	0.4557	3.0277	0.3004	0.5507	1
2.6535	0.4438	0.7106	2.5932	0.3780	0.6546	2
2.0541	0.6308	0.8575	2.2998	0.4556	0.6871	3
1.4622	0.7924	0.9496	2.0054	0.5056	0.7234	4
0.9098	0.9201	0.9887	1.8079	0.5695	0.7785	5
0.5220	0.9821	0.9969	1.6444	0.5845	0.7922	6
0.3104	0.9956	0.9981	1.6041	0.5770	0.8035	7

Framework versions

• Transformers 4.21.3
• TensorFlow 2.8.2
• Datasets 2.4.0
• Tokenizers 0.12.1