readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: vit-base-avengers-v1
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: imagefolder
        type: imagefolder
        args: avengers-dataset
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8683385579937304
  ---
  
  <!-- 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. -->
  
  # vit-base-avengers-v1
  
  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 imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5324
  - Accuracy: 0.8683
  
  Refer to this [medium article](https://medium.com/@dingusagar/marvel-character-classification-by-fine-tuning-vision-transformer-45c14a7d8719) for more info on how it was trained. 
  
  
  
  ## Limitations
  Training was done on google images for these search terms each representing a class.
  Iron Man,Captain America,Thor,Spider Man,Docter Strage,Black Panther,Ant Man,Captain Marvel,Hulk,Black Widow,Hawkeye Avengers,Scarlet Witch,Vision Avengers,Bucky Barnes,Falcon Avengers,Loki
  
  Therefore it has seen more of images where these super heros are in their suit or superhero outfit. 
  For example an image of hulk is detected correctly, but an image of Bruce Banner is not simply because the model has't seen those images. 
  A little bit of data augmentation will help. 
  
  ## 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: 0.0002
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.8183        | 1.27  | 100  | 1.0134          | 0.8464   |
  | 0.2234        | 2.53  | 200  | 0.6146          | 0.8495   |
  | 0.1206        | 3.8   | 300  | 0.5324          | 0.8683   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  