---
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](https://huggingface.co/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](https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Human%20Activity%20Recognition/Images/Sample%20Images.png)

## 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