readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: finetuned-ViT-human-action-recognition-v1
    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. -->
  
  # finetuned-ViT-human-action-recognition-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 Human_Action_Recognition dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.1427
  - Accuracy: 0.0791
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.4986        | 0.13  | 100  | 3.1427          | 0.0791   |
  | 1.1929        | 0.25  | 200  | 3.4083          | 0.0726   |
  | 1.2673        | 0.38  | 300  | 3.4615          | 0.0769   |
  | 0.9805        | 0.51  | 400  | 3.9192          | 0.0824   |
  | 1.158         | 0.63  | 500  | 4.2648          | 0.0698   |
  | 1.2544        | 0.76  | 600  | 4.5536          | 0.0574   |
  | 1.0073        | 0.89  | 700  | 4.0310          | 0.0819   |
  | 0.9315        | 1.02  | 800  | 4.5154          | 0.0702   |
  | 0.9063        | 1.14  | 900  | 4.7162          | 0.0633   |
  | 0.6756        | 1.27  | 1000 | 4.6482          | 0.0626   |
  | 1.0239        | 1.4   | 1100 | 4.6437          | 0.0635   |
  | 0.7634        | 1.52  | 1200 | 4.5625          | 0.0752   |
  | 0.8365        | 1.65  | 1300 | 4.9912          | 0.0561   |
  | 0.8979        | 1.78  | 1400 | 5.1739          | 0.0356   |
  | 0.9448        | 1.9   | 1500 | 4.8946          | 0.0541   |
  | 0.697         | 2.03  | 1600 | 4.9516          | 0.0741   |
  | 0.7861        | 2.16  | 1700 | 5.0090          | 0.0776   |
  | 0.6404        | 2.28  | 1800 | 5.3905          | 0.0643   |
  | 0.7939        | 2.41  | 1900 | 4.9159          | 0.1015   |
  | 0.6331        | 2.54  | 2000 | 5.3083          | 0.0589   |
  | 0.6082        | 2.66  | 2100 | 4.8538          | 0.0857   |
  | 0.6229        | 2.79  | 2200 | 5.3086          | 0.0689   |
  | 0.6964        | 2.92  | 2300 | 5.3745          | 0.0713   |
  | 0.5246        | 3.05  | 2400 | 5.0369          | 0.0796   |
  | 0.6097        | 3.17  | 2500 | 5.2935          | 0.0743   |
  | 0.5778        | 3.3   | 2600 | 5.5431          | 0.0709   |
  | 0.4196        | 3.43  | 2700 | 5.5508          | 0.0759   |
  | 0.5495        | 3.55  | 2800 | 5.5728          | 0.0813   |
  | 0.5932        | 3.68  | 2900 | 5.7992          | 0.0663   |
  | 0.4382        | 3.81  | 3000 | 5.8010          | 0.0643   |
  | 0.4827        | 3.93  | 3100 | 5.7529          | 0.0680   |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  