readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: vit-base-patch16-224-in21k_GI_diagnosis
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: imagefolder
        type: imagefolder
        config: default
        split: train
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9375
  ---
  
  <!-- 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-patch16-224-in21k_GI_diagnosis
  
  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: 0.2538
  - Accuracy: 0.9375
  - Weighted f1: 0.9365
  - Micro f1: 0.9375
  - Macro f1: 0.9365
  - Weighted recall: 0.9375
  - Micro recall: 0.9375
  - Macro recall: 0.9375
  - Weighted precision: 0.9455
  - Micro precision: 0.9375
  - Macro precision: 0.9455
  
  ## 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
  | 1.3805        | 1.0   | 200  | 0.5006          | 0.8638   | 0.8531      | 0.8638   | 0.8531   | 0.8638          | 0.8638       | 0.8638       | 0.9111             | 0.8638          | 0.9111          |
  | 1.3805        | 2.0   | 400  | 0.2538          | 0.9375   | 0.9365      | 0.9375   | 0.9365   | 0.9375          | 0.9375       | 0.9375       | 0.9455             | 0.9375          | 0.9455          |
  | 0.0628        | 3.0   | 600  | 0.5797          | 0.8812   | 0.8740      | 0.8812   | 0.8740   | 0.8812          | 0.8812       | 0.8813       | 0.9157             | 0.8812          | 0.9157          |
  
  
  ### Framework versions
  
  - Transformers 4.22.2
  - Pytorch 1.12.1
  - Datasets 2.5.2
  - Tokenizers 0.12.1
  