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

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: 3.2187
• Train Accuracy: 0.5652
• Train Top-3-accuracy: 0.7611
• Validation Loss: 3.8221
• Validation Accuracy: 0.2540
• Validation Top-3-accuracy: 0.4409
• 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': 3e-05, 'decay_steps': 4920, '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
5.3476	0.0283	0.0716	5.1306	0.0483	0.1240	0
4.9357	0.0914	0.2057	4.7998	0.1158	0.2385	1
4.6155	0.1641	0.3230	4.5616	0.1430	0.2891	2
4.3325	0.2269	0.4188	4.3480	0.1722	0.3391	3
4.0702	0.2915	0.4984	4.1662	0.2042	0.3886	4
3.8262	0.3638	0.5758	4.0416	0.2296	0.4067	5
3.6117	0.4258	0.6415	3.9451	0.2329	0.4234	6
3.4324	0.4855	0.6956	3.8690	0.2499	0.4397	7
3.2991	0.5320	0.7376	3.8351	0.2553	0.4359	8
3.2187	0.5652	0.7611	3.8221	0.2540	0.4409	9

Framework versions

• Transformers 4.21.2
• TensorFlow 2.8.2
• Datasets 2.4.0
• Tokenizers 0.12.1