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

# vivit-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: 1.5318
- Accuracy: 0.8134

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0323        | 0.1   | 1201  | 1.9695          | 0.6636   |
| 1.051         | 1.1   | 2402  | 1.8521          | 0.7380   |
| 0.0027        | 2.1   | 3603  | 1.9220          | 0.6500   |
| 0.0008        | 3.1   | 4804  | 1.3982          | 0.7767   |
| 1.1999        | 4.1   | 6005  | 1.5625          | 0.6924   |
| 0.0004        | 5.1   | 7206  | 1.4784          | 0.7614   |
| 0.0188        | 6.1   | 8407  | 1.3777          | 0.7882   |
| 0.6328        | 7.1   | 9608  | 1.6121          | 0.7761   |
| 0.8161        | 8.1   | 10809 | 1.7552          | 0.7670   |
| 0.0001        | 9.1   | 12010 | 1.5318          | 0.8134   |


### Framework versions

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