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

# Timesformers-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: 1.7694
- Accuracy: 0.7438

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0539        | 0.1   | 1201  | 2.3236          | 0.6307   |
| 0.5697        | 1.1   | 2402  | 1.9547          | 0.6739   |
| 0.5417        | 2.1   | 3603  | 1.7376          | 0.6951   |
| 0.0014        | 3.1   | 4804  | 1.8078          | 0.6920   |
| 1.1162        | 4.1   | 6005  | 1.7942          | 0.6921   |
| 0.0009        | 5.1   | 7206  | 1.4165          | 0.7779   |
| 0.0053        | 6.1   | 8407  | 1.7419          | 0.7540   |
| 1.4804        | 7.1   | 9608  | 1.5797          | 0.7424   |
| 0.6189        | 8.1   | 10809 | 1.9191          | 0.7305   |
| 0.0009        | 9.1   | 12010 | 1.7694          | 0.7438   |


### Framework versions

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