readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - cifar10
  metrics:
  - accuracy
  model-index:
  - name: dit-base-finetuned-rvlcdip-finetuned-eurosat
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: cifar10
        type: cifar10
        config: plain_text
        split: train
        args: plain_text
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.826
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # dit-base-finetuned-rvlcdip-finetuned-eurosat
  
  This model is a fine-tuned version of [microsoft/dit-base-finetuned-rvlcdip](https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip) on the cifar10 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5140
  - Accuracy: 0.826
  
  ## 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:
  - learning_rate: 5e-05
  - train_batch_size: 2
  - eval_batch_size: 2
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 8
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.1795        | 1.0   | 5625  | 0.8931          | 0.696    |
  | 0.9065        | 2.0   | 11250 | 0.6352          | 0.7908   |
  | 0.7088        | 3.0   | 16875 | 0.5140          | 0.826    |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  