readme: ---
  license: apache-2.0
  tags:
  - image-captioning
  - generated_from_trainer
  model-index:
  - name: ViTGPT2I2A
    results: []
  ---
  
  <!-- 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. -->
  
  # ViTGPT2I2A
  
  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the vizwiz dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0708
  
  ## 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: 2e-05
  - train_batch_size: 2
  - eval_batch_size: 2
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 2
  - total_train_batch_size: 4
  - total_eval_batch_size: 4
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 0.1528        | 0.17  | 1000  | 0.0869          |
  | 0.0899        | 0.34  | 2000  | 0.0817          |
  | 0.084         | 0.51  | 3000  | 0.0790          |
  | 0.0814        | 0.68  | 4000  | 0.0773          |
  | 0.0803        | 0.85  | 5000  | 0.0757          |
  | 0.077         | 1.02  | 6000  | 0.0745          |
  | 0.0739        | 1.19  | 7000  | 0.0740          |
  | 0.0719        | 1.37  | 8000  | 0.0737          |
  | 0.0717        | 1.54  | 9000  | 0.0730          |
  | 0.0731        | 1.71  | 10000 | 0.0727          |
  | 0.0708        | 1.88  | 11000 | 0.0720          |
  | 0.0697        | 2.05  | 12000 | 0.0717          |
  | 0.0655        | 2.22  | 13000 | 0.0719          |
  | 0.0653        | 2.39  | 14000 | 0.0719          |
  | 0.0657        | 2.56  | 15000 | 0.0712          |
  | 0.0663        | 2.73  | 16000 | 0.0710          |
  | 0.0654        | 2.9   | 17000 | 0.0708          |
  | 0.0645        | 3.07  | 18000 | 0.0716          |
  | 0.0616        | 3.24  | 19000 | 0.0712          |
  | 0.0607        | 3.41  | 20000 | 0.0712          |
  | 0.0611        | 3.58  | 21000 | 0.0711          |
  | 0.0615        | 3.76  | 22000 | 0.0711          |
  | 0.0614        | 3.93  | 23000 | 0.0710          |
  | 0.0594        | 4.1   | 24000 | 0.0716          |
  | 0.0587        | 4.27  | 25000 | 0.0715          |
  | 0.0574        | 4.44  | 26000 | 0.0715          |
  | 0.0579        | 4.61  | 27000 | 0.0715          |
  | 0.0581        | 4.78  | 28000 | 0.0715          |
  | 0.0579        | 4.95  | 29000 | 0.0715          |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.2+cu113
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  