---
library_name: transformers
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Vivit-d3
  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-d3

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: 0.2800
- Accuracy: 0.9509

## 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: 2240
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.113         | 0.1   | 224  | 0.2148          | 0.9464   |
| 3.4763        | 1.1   | 448  | 0.3339          | 0.8869   |
| 0.5019        | 2.1   | 672  | 0.3398          | 0.9449   |
| 0.0116        | 3.1   | 896  | 0.3553          | 0.9360   |
| 0.8144        | 4.1   | 1120 | 0.4592          | 0.9405   |
| 0.9856        | 5.1   | 1344 | 0.3184          | 0.9286   |
| 0.0005        | 6.1   | 1568 | 0.2253          | 0.9435   |
| 0.0001        | 7.1   | 1792 | 0.3713          | 0.9479   |
| 0.0           | 8.1   | 2016 | 0.3450          | 0.9479   |
| 0.2266        | 9.1   | 2240 | 0.2800          | 0.9509   |


### Framework versions

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