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videoMAE_kinetics_wlasl100_coR_30ep
Shawon16/videoMAE_kinetics_wlasl100_coR_30ep
videoMAE_kinetics_wlasl100_coR_30ep 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 cc-by-nc-4.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Video Classification
- Library
- transformers
- License
- cc-by-nc-4.0
- Model type
- videomae
- Access
- Public
- Created
- Dec 10, 2025
- Updated
- Dec 10, 2025
- SHA
- 8e4d635b
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. -->
videoMAE_kinetics_wlasl100_coR_30ep
This model is a fine-tuned version of MCG-NJU/videomae-base-finetuned-kinetics on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9436
- Accuracy: 0.7692
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: 5400
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 18.4812 | 0.0333 | 180 | 4.5735 | 0.0325 |
| 17.4488 | 1.0333 | 360 | 3.9865 | 0.1272 |
| 13.7391 | 2.0332 | 540 | 3.1402 | 0.4024 |
| 9.4991 | 3.0334 | 721 | 2.3636 | 0.5444 |
| 6.292 | 4.0333 | 901 | 1.9127 | 0.6213 |
| 3.993 | 5.0333 | 1081 | 1.5634 | 0.6923 |
| 2.452 | 6.0332 | 1261 | 1.4193 | 0.6893 |
| 1.4881 | 7.0334 | 1442 | 1.2840 | 0.7101 |
| 0.9036 | 8.0333 | 1622 | 1.1605 | 0.7426 |
| 0.5281 | 9.0333 | 1802 | 1.1299 | 0.7101 |
| 0.3228 | 10.0332 | 1982 | 1.1283 | 0.7189 |
| 0.1854 | 11.0334 | 2163 | 0.9530 | 0.7426 |
| 0.1304 | 12.0333 | 2343 | 1.0324 | 0.7604 |
| 0.0797 | 13.0333 | 2523 | 0.9985 | 0.7456 |
| 0.07 | 14.0332 | 2703 | 0.9905 | 0.7604 |
| 0.0681 | 15.0334 | 2884 | 0.9633 | 0.7574 |
| 0.0559 | 16.0333 | 3064 | 0.9113 | 0.7692 |
| 0.0548 | 17.0333 | 3244 | 0.9272 | 0.7544 |
| 0.0367 | 18.0332 | 3424 | 0.9505 | 0.7692 |
| 0.0636 | 19.0334 | 3605 | 0.9278 | 0.7633 |
| 0.0767 | 20.0333 | 3785 | 0.9333 | 0.7604 |
| 0.071 | 21.0333 | 3965 | 0.9436 | 0.7692 |
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
- videomae
- License
- cc-by-nc-4.0
- Created
- Dec 10, 2025
- Updated
- Dec 10, 2025