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ViViT_wlasl_100__signer_20ep_coR_
Shawon16/ViViT_wlasl_100__signer_20ep_coR_
ViViT_wlasl_100__signer_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 2, 2025
- Updated
- Dec 2, 2025
- SHA
- 0b90c75f
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_100__signer_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: 1.2104
- Accuracy: 0.6864
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: 3600
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 18.6783 | 0.05 | 180 | 4.5403 | 0.0414 |
| 16.851 | 1.0499 | 360 | 3.8917 | 0.1568 |
| 12.3649 | 2.0499 | 540 | 3.0026 | 0.3284 |
| 7.877 | 3.0501 | 721 | 2.3964 | 0.4852 |
| 4.8527 | 4.05 | 901 | 2.0190 | 0.5651 |
| 2.7599 | 5.0499 | 1081 | 1.7904 | 0.5976 |
| 1.516 | 6.0499 | 1261 | 1.5763 | 0.6272 |
| 0.8343 | 7.0501 | 1442 | 1.4678 | 0.6154 |
| 0.4144 | 8.05 | 1622 | 1.4205 | 0.6420 |
| 0.2624 | 9.0499 | 1802 | 1.2896 | 0.6716 |
| 0.1498 | 10.0499 | 1982 | 1.2734 | 0.6627 |
| 0.0906 | 11.0501 | 2163 | 1.2307 | 0.6805 |
| 0.0779 | 12.05 | 2343 | 1.2818 | 0.6805 |
| 0.0647 | 13.0499 | 2523 | 1.2184 | 0.6893 |
| 0.0615 | 14.0499 | 2703 | 1.2204 | 0.6953 |
| 0.0513 | 15.0501 | 2884 | 1.2155 | 0.6716 |
| 0.0456 | 16.05 | 3064 | 1.2052 | 0.6982 |
| 0.0219 | 17.0499 | 3244 | 1.2122 | 0.6864 |
| 0.033 | 18.0499 | 3424 | 1.2159 | 0.6893 |
| 0.0206 | 19.0487 | 3600 | 1.2104 | 0.6864 |
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 2, 2025
- Updated
- Dec 2, 2025