---
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: vivit-b-16x2-kinetics400-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-kinetics400-finetuned-cctv-surveillance

This model is a fine-tuned version of [google/vivit-b-16x2-kinetics400](https://huggingface.co/google/vivit-b-16x2-kinetics400) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1690
- Accuracy: 0.9559
- F1: 0.9430
- Recall: 0.9559
- Precision: 0.9333

## 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: 4032

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 1.5836        | 0.12  | 504  | 0.3644          | 0.9206   | 0.8850 | 0.9206 | 0.8799    |
| 0.3767        | 1.12  | 1008 | 0.2586          | 0.9265   | 0.8994 | 0.9265 | 0.8831    |
| 0.2063        | 2.12  | 1512 | 0.2190          | 0.9294   | 0.9097 | 0.9294 | 0.9002    |
| 0.4514        | 3.12  | 2016 | 0.2217          | 0.9529   | 0.9419 | 0.9529 | 0.9380    |
| 0.2678        | 4.12  | 2520 | 0.1919          | 0.9529   | 0.9419 | 0.9529 | 0.9380    |
| 0.2311        | 5.12  | 3024 | 0.1797          | 0.9412   | 0.9252 | 0.9412 | 0.9141    |
| 0.5256        | 6.12  | 3528 | 0.1690          | 0.9559   | 0.9430 | 0.9559 | 0.9333    |
| 0.539         | 7.12  | 4032 | 0.1678          | 0.9529   | 0.9398 | 0.9529 | 0.9297    |


### Framework versions

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