readme: ---
  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: 1.0000
  ---
  
  <!-- This model card has been generated automatically according to the information Keras had access to. You should
  probably proofread and complete it, then remove this comment. -->
  
  # philschmid/vit-base-patch16-224-in21k-euroSat
  
  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/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
  