---
library_name: transformers
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vivit-vivit-d2
  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-vivit-d2

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: 3.0210
- Accuracy: 0.3496

## 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: 1
- eval_batch_size: 1
- seed: 42
- 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: 6650
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.8445        | 0.1   | 665  | 2.2783          | 0.2615   |
| 2.1562        | 1.1   | 1330 | 2.2367          | 0.3635   |
| 2.2654        | 2.1   | 1995 | 1.9989          | 0.4808   |
| 2.0538        | 3.1   | 2660 | 2.4479          | 0.3778   |
| 1.1013        | 4.1   | 3325 | 2.5521          | 0.3876   |
| 0.8297        | 5.1   | 3990 | 2.8096          | 0.3955   |
| 0.5534        | 6.1   | 4655 | 2.7355          | 0.3491   |
| 0.3153        | 7.1   | 5320 | 3.0770          | 0.3477   |
| 0.6072        | 8.1   | 5985 | 2.8821          | 0.3477   |
| 1.3843        | 9.1   | 6650 | 3.0210          | 0.3496   |


### Framework versions

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