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vit-base-patch16-224-in21k_Human_Activity_Recognition

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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
  - recall
  - precision
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
language:
  - en

vit-base-patch16-224-in21k_Human_Activity_Recognition

This model is a fine-tuned version of google/vit-base-patch16-224-in21k.

It achieves the following results on the evaluation set:

  • Loss: 0.7403
  • Accuracy: 0.8381
  • F1
    • Weighted: 0.8388
    • Micro: 0.8381
    • Macro: 0.8394
  • Recall
    • Weighted: 0.8381
    • Micro: 0.8381
    • Macro: 0.8390
  • Precision
    • Weighted: 0.8421
    • Micro: 0.8381
    • Macro: 0.8424

Model description

This is a multiclass image classification model of humans doing different activities.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Human%20Activity%20Recognition/ViT-Human%20Action_Recogniton.ipynb

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology. You are welcome to test and experiment with this model, but it is at your own risk/peril.

Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/meetnagadia/human-action-recognition-har-dataset

Sample Images From Dataset:

Sample Images

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