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---
library_name: transformers
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: ViViT_WLASL_250_epochs
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_250_epochs
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: 4.0544
- Top 1 Accuracy: 0.2617
- Top 5 Accuracy: 0.5577
- Top 10 Accuracy: 0.6670
- Accuracy: 0.2617
- Precision: 0.2325
- Recall: 0.2617
- F1: 0.2253
## 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: 893000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Top 1 Accuracy | Top 5 Accuracy | Top 10 Accuracy | Accuracy | Precision | Recall | F1 |
|:-------------:|:-------:|:-----:|:---------------:|:--------------:|:--------------:|:---------------:|:--------:|:---------:|:------:|:------:|
| 30.5598 | 0.004 | 3572 | 7.6528 | 0.0010 | 0.0038 | 0.0064 | 0.0010 | 0.0008 | 0.0010 | 0.0004 |
| 29.9841 | 1.0040 | 7144 | 7.5548 | 0.0046 | 0.0120 | 0.0176 | 0.0046 | 0.0006 | 0.0046 | 0.0009 |
| 28.2597 | 2.0040 | 10716 | 7.2959 | 0.0125 | 0.0337 | 0.0495 | 0.0125 | 0.0053 | 0.0125 | 0.0048 |
| 26.1127 | 3.0040 | 14289 | 6.9165 | 0.0304 | 0.0748 | 0.1223 | 0.0301 | 0.0108 | 0.0301 | 0.0120 |
| 23.7044 | 4.004 | 17861 | 6.4996 | 0.0447 | 0.1407 | 0.2102 | 0.0447 | 0.0182 | 0.0447 | 0.0196 |
| 20.6604 | 5.0040 | 21433 | 6.0328 | 0.0822 | 0.2288 | 0.3121 | 0.0822 | 0.0421 | 0.0822 | 0.0434 |
| 17.6287 | 6.0040 | 25005 | 5.5622 | 0.1210 | 0.3041 | 0.4213 | 0.1210 | 0.0714 | 0.1210 | 0.0742 |
| 14.3215 | 7.0040 | 28578 | 5.0794 | 0.1576 | 0.3797 | 0.4951 | 0.1573 | 0.0998 | 0.1573 | 0.1038 |
| 10.5032 | 8.004 | 32150 | 4.6439 | 0.1915 | 0.4494 | 0.5695 | 0.1915 | 0.1353 | 0.1915 | 0.1386 |
| 7.2387 | 9.0040 | 35722 | 4.2461 | 0.2247 | 0.5123 | 0.6297 | 0.2255 | 0.1676 | 0.2255 | 0.1721 |
| 3.9708 | 10.0040 | 39294 | 3.9632 | 0.2485 | 0.5587 | 0.6701 | 0.2487 | 0.2034 | 0.2487 | 0.2046 |
| 2.1244 | 11.0040 | 42867 | 3.7748 | 0.2587 | 0.5753 | 0.6872 | 0.2587 | 0.2258 | 0.2587 | 0.2220 |
| 1.3992 | 12.004 | 46439 | 3.6907 | 0.2543 | 0.5794 | 0.6885 | 0.2543 | 0.2279 | 0.2543 | 0.2210 |
| 1.0175 | 13.0040 | 50011 | 3.7060 | 0.2503 | 0.5738 | 0.6874 | 0.2503 | 0.2176 | 0.2503 | 0.2142 |
| 0.914 | 14.0040 | 53583 | 3.6819 | 0.2648 | 0.5804 | 0.6915 | 0.2648 | 0.2380 | 0.2648 | 0.2311 |
| 0.7522 | 15.0040 | 57156 | 3.7360 | 0.2561 | 0.5758 | 0.6969 | 0.2564 | 0.2325 | 0.2564 | 0.2235 |
| 1.045 | 16.004 | 60728 | 3.7846 | 0.2638 | 0.5723 | 0.6877 | 0.2635 | 0.2470 | 0.2635 | 0.2327 |
| 0.8234 | 17.0040 | 64300 | 3.8910 | 0.2574 | 0.5692 | 0.6724 | 0.2572 | 0.2386 | 0.2572 | 0.2261 |
| 0.7311 | 18.0040 | 67872 | 4.0142 | 0.2561 | 0.5585 | 0.6680 | 0.2561 | 0.2402 | 0.2561 | 0.2262 |
| 1.0981 | 19.0040 | 71445 | 4.0544 | 0.2617 | 0.5577 | 0.6670 | 0.2617 | 0.2325 | 0.2617 | 0.2253 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.1
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