---
library_name: transformers
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Timesformer-vivit-d1
  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. -->

# Timesformer-vivit-d1

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: 1.7607
- Accuracy: 0.7557

## 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: 12010
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0024        | 0.1   | 1201  | 2.5898          | 0.6116   |
| 0.7957        | 1.1   | 2402  | 1.8821          | 0.6666   |
| 0.5344        | 2.1   | 3603  | 1.7371          | 0.6686   |
| 0.2148        | 3.1   | 4804  | 1.4470          | 0.7413   |
| 0.883         | 4.1   | 6005  | 1.7974          | 0.6735   |
| 0.0012        | 5.1   | 7206  | 1.5739          | 0.7386   |
| 0.0008        | 6.1   | 8407  | 1.7734          | 0.7307   |
| 1.8254        | 7.1   | 9608  | 1.4496          | 0.7704   |
| 0.6005        | 8.1   | 10809 | 1.8740          | 0.7504   |
| 0.0002        | 9.1   | 12010 | 1.7607          | 0.7557   |


### Framework versions

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