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---
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:_
## 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 |