readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: vit-base-patch16-224-in21k_Human_Activity_Recognition
    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: 0.8380952380952381
  ---
  
  <!-- 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_Human_Activity_Recognition
  
  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: 0.7403
  - Accuracy: 0.8381
  - Weighted f1: 0.8388
  - Micro f1: 0.8381
  - Macro f1: 0.8394
  - Weighted recall: 0.8381
  - Micro recall: 0.8381
  - Macro recall: 0.8390
  - Weighted precision: 0.8421
  - Micro precision: 0.8381
  - Macro precision: 0.8424
  
  ## 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: 5
  
  ### 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
  | 1.0814        | 1.0   | 630  | 0.7368          | 0.7794   | 0.7795      | 0.7794   | 0.7798   | 0.7794          | 0.7794       | 0.7797       | 0.7896             | 0.7794          | 0.7896          |
  | 0.5149        | 2.0   | 1260 | 0.6439          | 0.8060   | 0.8049      | 0.8060   | 0.8036   | 0.8060          | 0.8060       | 0.8051       | 0.8136             | 0.8060          | 0.8130          |
  | 0.3023        | 3.0   | 1890 | 0.7026          | 0.8254   | 0.8272      | 0.8254   | 0.8278   | 0.8254          | 0.8254       | 0.8256       | 0.8335             | 0.8254          | 0.8345          |
  | 0.0507        | 4.0   | 2520 | 0.7414          | 0.8317   | 0.8342      | 0.8317   | 0.8348   | 0.8317          | 0.8317       | 0.8321       | 0.8427             | 0.8317          | 0.8438          |
  | 0.0128        | 5.0   | 3150 | 0.7403          | 0.8381   | 0.8388      | 0.8381   | 0.8394   | 0.8381          | 0.8381       | 0.8390       | 0.8421             | 0.8381          | 0.8424          |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.12.1
  - Datasets 2.8.0
  - Tokenizers 0.12.1
  