README.md

--- 
license: mit 
base_model: google/vivit-b-16x2-kinetics400 
tags: 
- generated_from_trainer 
metrics: 
- accuracy 
model-index: 
- name: vivit-b-16x2-kinetics400-ft-48192 
 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-48192 
 
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: 0.7001 
- Accuracy: 0.7302 
 
## 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 | 
|:-------------:|:-------:|:----:|:---------------:|:--------:| 
| 1.0936 | 0.0202 | 111 | 1.1342 | 0.3280 | 
| 1.0023 | 1.0202 | 222 | 0.9602 | 0.5291 | 
| 1.1132 | 2.0202 | 333 | 1.0361 | 0.4603 | 
| 0.9459 | 3.0202 | 444 | 1.0181 | 0.4603 | 
| 0.9839 | 4.0202 | 555 | 0.9418 | 0.5767 | 
| 1.0077 | 5.0202 | 666 | 0.9735 | 0.5344 | 
| 1.046 | 6.0202 | 777 | 0.8630 | 0.6032 | 
| 1.2237 | 7.0202 | 888 | 0.8513 | 0.6455 | 
| 0.8309 | 8.0202 | 999 | 1.0534 | 0.5344 | 
| 0.9748 | 9.0202 | 1110 | 0.8717 | 0.6402 | 
| 0.9053 | 10.0202 | 1221 | 0.9555 | 0.5291 | 
| 0.8577 | 11.0202 | 1332 | 0.9219 | 0.5820 | 
| 0.9806 | 12.0202 | 1443 | 0.9808 | 0.5397 | 
| 0.7957 | 13.0202 | 1554 | 0.9395 | 0.5820 | 
| 0.787 | 14.0202 | 1665 | 0.8645 | 0.5979 | 
| 0.8045 | 15.0202 | 1776 | 0.9837 | 0.5450 | 
| 0.7793 | 16.0202 | 1887 | 0.9503 | 0.5767 | 
| 0.68 | 17.0202 | 1998 | 0.8213 | 0.6667 | 
| 0.8641 | 18.0202 | 2109 | 0.8046 | 0.6772 | 
| 0.6624 | 19.0202 | 2220 | 0.7309 | 0.6984 | 
| 0.6083 | 20.0202 | 2331 | 0.8542 | 0.6508 | 
| 0.6252 | 21.0202 | 2442 | 0.7641 | 0.6878 | 
| 0.6654 | 22.0202 | 2553 | 0.8472 | 0.6931 | 
| 0.5135 | 23.0202 | 2664 | 0.7514 | 0.6614 | 
| 0.6607 | 24.0202 | 2775 | 0.9482 | 0.5979 | 
| 0.6489 | 25.0202 | 2886 | 1.7302 | 0.4497 | 
| 0.5558 | 26.0202 | 2997 | 1.1263 | 0.5661 | 
| 0.7327 | 27.0202 | 3108 | 0.7243 | 0.6878 | 
| 0.5886 | 28.0202 | 3219 | 0.6610 | 0.7143 | 
| 0.7143 | 29.0202 | 3330 | 0.7716 | 0.6667 | 
| 0.5154 | 30.0202 | 3441 | 0.9681 | 0.6138 | 
| 0.5505 | 31.0202 | 3552 | 0.9641 | 0.6349 | 
| 0.6 | 32.0202 | 3663 | 0.7182 | 0.6984 | 
| 0.6814 | 33.0202 | 3774 | 0.7108 | 0.7090 | 
| 0.7797 | 34.0202 | 3885 | 0.7822 | 0.6984 | 
| 0.5997 | 35.0202 | 3996 | 0.7895 | 0.6984 | 
| 0.6454 | 36.0202 | 4107 | 0.8025 | 0.7090 | 
| 0.5421 | 37.0202 | 4218 | 0.7742 | 0.6825 | 
| 0.5026 | 38.0202 | 4329 | 0.6991 | 0.7302 | 
| 0.5058 | 39.0202 | 4440 | 0.7855 | 0.6667 | 
| 0.5395 | 40.0202 | 4551 | 0.7164 | 0.7196 | 
| 0.5196 | 41.0202 | 4662 | 0.8336 | 0.6931 | 
| 0.3783 | 42.0202 | 4773 | 0.7769 | 0.7143 | 
| 0.5183 | 43.0202 | 4884 | 0.8074 | 0.6984 | 
| 0.4013 | 44.0202 | 4995 | 0.7954 | 0.7196 | 
| 0.5218 | 45.0202 | 5106 | 0.7939 | 0.7090 | 
| 0.52 | 46.0202 | 5217 | 0.7603 | 0.7143 | 
| 0.409 | 47.0202 | 5328 | 0.8217 | 0.7143 | 
| 0.4437 | 48.0202 | 5439 | 0.8168 | 0.7143 | 
| 0.415 | 49.0111 | 5500 | 0.8344 | 0.7196 | 
 
 
### Framework versions 
 
- Transformers 4.41.2 
- Pytorch 1.13.0+cu117 
- Datasets 2.20.0 
- Tokenizers 0.19.1