---
library_name: transformers
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ViViT_wlasl_2000_20ep_coR
  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_wlasl_2000_20ep_coR

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: 3.2256
- Accuracy: 0.3437

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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: 35720
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 30.7198       | 0.05    | 1786  | 7.5765          | 0.0023   |
| 29.0292       | 1.0500  | 3572  | 6.7339          | 0.0255   |
| 24.0941       | 2.0500  | 5358  | 5.7016          | 0.0889   |
| 18.973        | 3.0500  | 7145  | 4.8953          | 0.1604   |
| 14.3207       | 4.05    | 8931  | 4.2779          | 0.2188   |
| 10.2288       | 5.0500  | 10717 | 3.8242          | 0.2561   |
| 6.8987        | 6.0500  | 12503 | 3.4816          | 0.2990   |
| 4.4195        | 7.0500  | 14290 | 3.3354          | 0.3105   |
| 2.8005        | 8.05    | 16076 | 3.2289          | 0.3212   |
| 1.8191        | 9.0500  | 17862 | 3.1795          | 0.3200   |
| 1.2778        | 10.0500 | 19648 | 3.1637          | 0.3292   |
| 1.0009        | 11.0500 | 21435 | 3.1523          | 0.3299   |
| 0.8082        | 12.05   | 23221 | 3.1508          | 0.3292   |
| 0.7047        | 13.0500 | 25007 | 3.1626          | 0.3276   |
| 0.6152        | 14.0500 | 26793 | 3.1711          | 0.3327   |
| 0.545         | 15.0500 | 28580 | 3.2040          | 0.3394   |
| 0.4952        | 16.05   | 30366 | 3.1936          | 0.3424   |
| 0.4463        | 17.0500 | 32152 | 3.2133          | 0.3435   |
| 0.403         | 18.0500 | 33938 | 3.2240          | 0.3432   |
| 0.3506        | 19.0499 | 35720 | 3.2256          | 0.3437   |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.1