satyam2652/vit-base-patch16-224-in21k-euroSat

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: 3.1431
• Train Accuracy: 1.0
• Train Top-3-accuracy: 1.0
• Validation Loss: 3.6270
• Validation Accuracy: 0.9566
• Validation Top-3-accuracy: 0.9948
• 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': 1680, '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
5.3716	0.1168	0.1875	5.2023	0.3802	0.5556	0
4.8783	0.7768	0.8929	4.7966	0.7257	0.9115	1
4.4274	0.9360	0.9933	4.4790	0.8212	0.9618	2
4.0789	0.9769	0.9985	4.2315	0.8837	0.9740	3
3.8025	0.9926	1.0	4.0327	0.9062	0.9878	4
3.5840	0.9970	1.0	3.8840	0.9253	0.9913	5
3.4123	1.0	1.0	3.7695	0.9392	0.9931	6
3.2826	1.0	1.0	3.6876	0.9462	0.9948	7
3.1937	1.0	1.0	3.6414	0.9583	0.9948	8
3.1431	1.0	1.0	3.6270	0.9566	0.9948	9

Framework versions

• Transformers 4.37.2
• TensorFlow 2.15.0
• Datasets 2.16.1
• Tokenizers 0.15.1