metadata

license: apache-2.0
tags:
  - generated_from_keras_callback
widget:
  - src: >-
      https://cdn.prod.www.spiegel.de/images/6b1135cd-0001-0004-0000-000000867699_w996_r1.778_fpx50_fpy47.38.jpg
metrics:
  - accuracy
model-index:
  - name: philschmid/vit-base-patch16-224-in21k-euroSat
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: eurosat
          type: eurosat
          args: default
        metrics:
          - name: accuracy
            type: accuracy
            value: 0.9906
          - name: top-3-accuracy
            type: top-3-accuracy
            value: 1

philschmid/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: 0.0218
Train Accuracy: 0.9990
Train Top-3-accuracy: 1.0000
Validation Loss: 0.0440
Validation Accuracy: 0.9906
Validation Top-3-accuracy: 1.0
Epoch: 5

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': 3585, '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
0.4692	0.9471	0.9878	0.1455	0.9861	0.9998	1
0.0998	0.9888	0.9996	0.0821	0.9864	0.9995	2
0.0517	0.9939	0.9999	0.0617	0.9871	1.0	3
0.0309	0.9971	0.9999	0.0524	0.9878	0.9998	4
0.0218	0.9990	1.0000	0.0440	0.9906	1.0	5

Framework versions

Transformers 4.15.0
TensorFlow 2.7.0
Datasets 1.17.0
Tokenizers 0.10.3