---
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vivit-b-16x2-kinetics400-ft-76388
  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-b-16x2-kinetics400-ft-76388

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: 0.9924
- Accuracy: 0.5595

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 5500

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.1083        | 0.0202  | 111  | 1.1112          | 0.3347   |
| 1.0789        | 1.0202  | 222  | 1.0576          | 0.4259   |
| 1.0767        | 2.0202  | 333  | 1.0863          | 0.4246   |
| 1.1114        | 3.0202  | 444  | 1.1061          | 0.3704   |
| 1.0832        | 4.0202  | 555  | 1.0536          | 0.4193   |
| 1.0622        | 5.0202  | 666  | 1.0720          | 0.4577   |
| 1.0874        | 6.0202  | 777  | 1.0304          | 0.4709   |
| 0.9742        | 7.0202  | 888  | 1.0340          | 0.4511   |
| 0.9848        | 8.0202  | 999  | 1.0367          | 0.4669   |
| 1.12          | 9.0202  | 1110 | 1.0269          | 0.4193   |
| 1.0484        | 10.0202 | 1221 | 1.0105          | 0.4511   |
| 0.9445        | 11.0202 | 1332 | 1.0052          | 0.4881   |
| 1.032         | 12.0202 | 1443 | 1.0365          | 0.4524   |
| 0.987         | 13.0202 | 1554 | 1.0019          | 0.5106   |
| 1.0797        | 14.0202 | 1665 | 1.0128          | 0.4656   |
| 0.9196        | 15.0202 | 1776 | 1.0431          | 0.5013   |
| 1.0727        | 16.0202 | 1887 | 1.0016          | 0.5344   |
| 0.9481        | 17.0202 | 1998 | 0.9983          | 0.5265   |
| 0.9034        | 18.0202 | 2109 | 1.0221          | 0.5013   |
| 0.8569        | 19.0202 | 2220 | 0.9825          | 0.5265   |
| 0.9256        | 20.0202 | 2331 | 0.9678          | 0.5397   |
| 1.0311        | 21.0202 | 2442 | 0.9574          | 0.5106   |
| 0.8651        | 22.0202 | 2553 | 1.0048          | 0.4987   |
| 0.9384        | 23.0202 | 2664 | 0.9717          | 0.5225   |
| 0.9545        | 24.0202 | 2775 | 0.9763          | 0.5172   |
| 0.9187        | 25.0202 | 2886 | 0.9628          | 0.5212   |
| 0.7953        | 26.0202 | 2997 | 0.9523          | 0.5265   |
| 0.8793        | 27.0202 | 3108 | 0.9977          | 0.5370   |
| 0.7897        | 28.0202 | 3219 | 0.9965          | 0.5317   |
| 0.8034        | 29.0202 | 3330 | 0.9272          | 0.5463   |
| 0.8469        | 30.0202 | 3441 | 0.9231          | 0.5384   |
| 0.79          | 31.0202 | 3552 | 0.9281          | 0.5728   |
| 0.8516        | 32.0202 | 3663 | 0.9310          | 0.5569   |
| 0.8138        | 33.0202 | 3774 | 0.9582          | 0.5675   |
| 0.8322        | 34.0202 | 3885 | 0.9741          | 0.5622   |
| 0.8064        | 35.0202 | 3996 | 0.9573          | 0.5754   |
| 0.8767        | 36.0202 | 4107 | 0.9290          | 0.5714   |
| 0.7978        | 37.0202 | 4218 | 0.9449          | 0.5728   |
| 0.8113        | 38.0202 | 4329 | 0.9493          | 0.5780   |
| 0.8065        | 39.0202 | 4440 | 0.9015          | 0.5926   |
| 0.7989        | 40.0202 | 4551 | 0.9139          | 0.5886   |
| 0.6323        | 41.0202 | 4662 | 0.9004          | 0.5992   |
| 0.6847        | 42.0202 | 4773 | 0.9083          | 0.6124   |
| 0.7711        | 43.0202 | 4884 | 0.9023          | 0.5979   |
| 0.5815        | 44.0202 | 4995 | 0.9247          | 0.6058   |
| 0.8821        | 45.0202 | 5106 | 0.9071          | 0.6058   |
| 0.7436        | 46.0202 | 5217 | 0.8924          | 0.6085   |
| 0.6863        | 47.0202 | 5328 | 0.8965          | 0.6111   |
| 0.7035        | 48.0202 | 5439 | 0.8941          | 0.6045   |
| 0.6348        | 49.0111 | 5500 | 0.8950          | 0.6124   |


### Framework versions

- Transformers 4.41.2
- Pytorch 1.13.0+cu117
- Datasets 2.20.0
- Tokenizers 0.19.1