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---
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
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