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# ViT-threat-classification-v2

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
This is model created as a prrof of concept for a Carleton University computer vision event. It is by no means meant to be used in deliverable systems in its current state, and should be used exclusively for research and development.
It achieves the following results on the evaluation set:
- Loss: 0.0381
- F1: 0.9657
- Precision: 0.9563
- Recall: 0.9752

## Model description

More information needed

## Intended uses & limitations

More information needed

## Collaborators

[Angus Bailey](https://huggingface.co/boshy)

[Thomas Nolasque](https://github.com/thomasnol)


## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:------:|:---------:|:------:|
| 0.0744        | 0.9985 | 326  | 0.0576          | 0.9466 | 0.9738    | 0.9208 |
| 0.0449        | 2.0    | 653  | 0.0397          | 0.9641 | 0.9747    | 0.9538 |
| 0.0207        | 2.9985 | 979  | 0.0409          | 0.9647 | 0.9607    | 0.9686 |
| 0.0342        | 4.0    | 1306 | 0.0382          | 0.9650 | 0.9518    | 0.9785 |
| 0.0286        | 4.9923 | 1630 | 0.0381          | 0.9657 | 0.9563    | 0.9752 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3

non-threat threat