---
license: mit
base_model: google/vivit-b-16x2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: vivit-b-16x2-finetuned-cctv-surveillance
  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. -->

# vivit-b-16x2-finetuned-cctv-surveillance

This model is a fine-tuned version of [google/vivit-b-16x2](https://huggingface.co/google/vivit-b-16x2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1478
- Accuracy: 0.9460
- F1: 0.9430
- Recall: 0.9460
- Precision: 0.9454

## 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: 5e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 4176

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.9564        | 0.12  | 522  | 0.4417          | 0.8685   | 0.8096 | 0.8685 | 0.7990    |
| 0.4574        | 1.12  | 1044 | 0.2633          | 0.9131   | 0.9042 | 0.9131 | 0.9269    |
| 0.421         | 2.12  | 1566 | 0.1875          | 0.9272   | 0.9100 | 0.9272 | 0.9353    |
| 0.4785        | 3.12  | 2088 | 0.1854          | 0.9249   | 0.9082 | 0.9249 | 0.9140    |
| 0.3213        | 4.12  | 2610 | 0.1805          | 0.9272   | 0.9125 | 0.9272 | 0.9216    |
| 0.1465        | 5.12  | 3132 | 0.1733          | 0.9413   | 0.9362 | 0.9413 | 0.9398    |
| 0.0784        | 6.12  | 3654 | 0.1616          | 0.9437   | 0.9391 | 0.9437 | 0.9434    |
| 0.2017        | 7.12  | 4176 | 0.1478          | 0.9460   | 0.9430 | 0.9460 | 0.9454    |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2