Imene/vit-base-patch16-224-wi2

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: 0.3098
• Train Accuracy: 0.9821
• Train Top-5-accuracy: 0.9971
• Validation Loss: 3.0737
• Validation Accuracy: 0.2491
• Validation Top-5-accuracy: 0.4476
• 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0003, 'decay_steps': 1750, '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': 0.001}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
• training_precision: mixed_float16

Training results

Train Loss	Train Accuracy	Train Top-5-accuracy	Validation Loss	Validation Accuracy	Validation Top-5-accuracy	Epoch
4.4859	0.0195	0.0579	4.2995	0.0368	0.0865	0
4.1729	0.0355	0.0987	4.0916	0.0472	0.1266	1
3.9541	0.0666	0.1641	3.8050	0.0781	0.2035	2
3.5823	0.1247	0.2615	3.4015	0.1429	0.2950	3
3.0156	0.1913	0.3987	3.0598	0.1880	0.3916	4
2.4618	0.3077	0.5572	2.9869	0.2056	0.4129	5
1.8979	0.4541	0.7165	2.9507	0.2298	0.4425	6
1.2075	0.6914	0.8886	3.0106	0.2394	0.4425	7
0.6026	0.9097	0.9810	3.0739	0.2428	0.4413	8
0.3098	0.9821	0.9971	3.0737	0.2491	0.4476	9

Framework versions

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