---
library_name: transformers
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: ViViT_LSA64_SR_6
  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_LSA64_SR_6

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: 0.0190
- Accuracy: 0.9961
- Precision: 0.9969
- Recall: 0.9961
- F1: 0.9960

## 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-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use 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
- training_steps: 8640
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 15.7817       | 0.0333 | 288  | 2.8824          | 0.4609   | 0.5309    | 0.4609 | 0.4307 |
| 4.7558        | 1.0333 | 576  | 0.5582          | 0.9492   | 0.9586    | 0.9492 | 0.9470 |
| 0.5173        | 2.0333 | 864  | 0.0999          | 0.9805   | 0.9854    | 0.9805 | 0.9798 |
| 0.1244        | 3.0333 | 1152 | 0.0102          | 1.0      | 1.0       | 1.0    | 1.0    |
| 0.0043        | 4.0333 | 1440 | 0.0265          | 0.9922   | 0.9938    | 0.9922 | 0.9921 |
| 0.021         | 5.0333 | 1728 | 0.0200          | 0.9922   | 0.9938    | 0.9922 | 0.9921 |
| 0.0014        | 6.0333 | 2016 | 0.0012          | 1.0      | 1.0       | 1.0    | 1.0    |
| 0.0414        | 7.0333 | 2304 | 0.0075          | 0.9961   | 0.9969    | 0.9961 | 0.9960 |
| 0.0386        | 8.0333 | 2592 | 0.0190          | 0.9961   | 0.9969    | 0.9961 | 0.9960 |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.1