|
---
|
|
license: mit
|
|
base_model: google/vivit-b-16x2-kinetics400
|
|
tags:
|
|
- generated_from_trainer
|
|
metrics:
|
|
- accuracy
|
|
model-index:
|
|
- name: cont-vvt-gs-rot-flip-wtoken-f198-4.4-h768-t8.16.16
|
|
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. -->
|
|
|
|
# cont-vvt-gs-rot-flip-wtoken-f198-4.4-h768-t8.16.16
|
|
|
|
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: 0.6569
|
|
- Accuracy: 0.7407
|
|
|
|
## 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: 2e-06
|
|
- train_batch_size: 4
|
|
- eval_batch_size: 4
|
|
- 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 |
|
|
|:-------------:|:-------:|:----:|:---------------:|:--------:|
|
|
| 0.868 | 0.0402 | 221 | 0.7216 | 0.6931 |
|
|
| 0.715 | 1.0402 | 442 | 0.6954 | 0.7037 |
|
|
| 0.7041 | 2.0402 | 663 | 0.7269 | 0.7460 |
|
|
| 0.5228 | 3.0402 | 884 | 0.7158 | 0.7407 |
|
|
| 0.7336 | 4.0402 | 1105 | 0.6833 | 0.7196 |
|
|
| 0.5987 | 5.0402 | 1326 | 0.6155 | 0.7725 |
|
|
| 0.8574 | 6.0402 | 1547 | 0.6601 | 0.7302 |
|
|
| 0.6805 | 7.0402 | 1768 | 0.6374 | 0.7460 |
|
|
| 0.8086 | 8.0402 | 1989 | 0.6896 | 0.6984 |
|
|
| 0.6552 | 9.0402 | 2210 | 0.6535 | 0.7090 |
|
|
| 0.7846 | 10.0402 | 2431 | 0.6646 | 0.7354 |
|
|
| 0.6114 | 11.0402 | 2652 | 0.6111 | 0.7619 |
|
|
| 0.7435 | 12.0402 | 2873 | 0.6779 | 0.7354 |
|
|
| 0.7742 | 13.0402 | 3094 | 0.7390 | 0.6878 |
|
|
| 0.6558 | 14.0402 | 3315 | 0.6284 | 0.7354 |
|
|
| 0.4822 | 15.0402 | 3536 | 0.7071 | 0.7196 |
|
|
| 0.7686 | 16.0402 | 3757 | 0.6982 | 0.7302 |
|
|
| 0.7945 | 17.0402 | 3978 | 0.6336 | 0.7566 |
|
|
| 0.5755 | 18.0402 | 4199 | 0.5924 | 0.7460 |
|
|
| 0.6895 | 19.0402 | 4420 | 0.6227 | 0.7513 |
|
|
| 0.4775 | 20.0402 | 4641 | 0.5846 | 0.7672 |
|
|
| 0.8137 | 21.0402 | 4862 | 0.6724 | 0.7354 |
|
|
| 0.4226 | 22.0402 | 5083 | 0.6772 | 0.7460 |
|
|
| 0.6616 | 23.0402 | 5304 | 0.6856 | 0.7407 |
|
|
| 0.5246 | 24.0356 | 5500 | 0.6569 | 0.7407 |
|
|
|
|
|
|
### Framework versions
|
|
|
|
- Transformers 4.41.2
|
|
- Pytorch 1.13.0+cu117
|
|
- Datasets 2.20.0
|
|
- Tokenizers 0.19.1
|
|
|
