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ViViT_BDSLW60_U5_20_coR
Shawon16/ViViT_BDSLW60_U5_20_coR
ViViT_BDSLW60_U5_20_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 4, 2025
- SHA
- f23cc2a3
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_BDSLW60_U5_20_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: 0.5355
- Accuracy: 0.8717
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: 18560
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 11.1492 | 0.0501 | 929 | 1.4489 | 0.7117 |
| 1.051 | 1.0501 | 1858 | 0.6573 | 0.7883 |
| 0.1849 | 2.0501 | 2787 | 0.7059 | 0.8117 |
| 0.1169 | 3.0501 | 3716 | 0.6554 | 0.8233 |
| 0.1042 | 4.0501 | 4645 | 0.8616 | 0.79 |
| 0.1028 | 5.0501 | 5574 | 0.8305 | 0.8267 |
| 0.0464 | 6.0501 | 6503 | 0.9667 | 0.8083 |
| 0.068 | 7.0501 | 7432 | 1.0300 | 0.7767 |
| 0.0957 | 8.0501 | 8361 | 0.8642 | 0.81 |
| 0.0562 | 9.0501 | 9290 | 1.0133 | 0.83 |
| 0.023 | 10.0501 | 10219 | 0.8639 | 0.8367 |
| 0.0526 | 11.0501 | 11148 | 0.6815 | 0.86 |
| 0.0445 | 12.0501 | 12077 | 0.6488 | 0.8633 |
| 0.0112 | 13.0501 | 13006 | 0.5026 | 0.8833 |
| 0.0028 | 14.0501 | 13935 | 0.8986 | 0.8367 |
| 0.0534 | 15.0501 | 14864 | 0.5898 | 0.8567 |
| 0.009 | 16.0501 | 15793 | 0.6852 | 0.8233 |
| 0.0086 | 17.0501 | 16722 | 0.4841 | 0.8683 |
| 0.0108 | 18.0501 | 17651 | 0.5355 | 0.8717 |
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 4, 2025