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ViViT_wlasl_2000_20ep_coR
Shawon16/ViViT_wlasl_2000_20ep_coR
ViViT_wlasl_2000_20ep_coR is a video classification model from Shawon16. Use it for the video classification task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Video Classification
- Library
- transformers
- License
- mit
- Model type
- vivit
- Access
- Public
- Created
- Dec 3, 2025
- Updated
- Dec 3, 2025
- SHA
- 6cfc99f0
Base models
Model card
From the Hugging Face model README
<!-- 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 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
- Task
- Video Classification
- Library
- transformers
- Type
- vivit
- License
- mit
- Created
- Dec 3, 2025
- Updated
- Dec 3, 2025