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videomae-base-finetuned-ucf101-subset
aleenah/videomae-base-finetuned-ucf101-subset
videomae-base-finetuned-ucf101-subset is a video classification model from aleenah. 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
- Jul 2, 2024
- Updated
- Jul 3, 2024
- SHA
- 518f3cce
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-base-finetuned-ucf101-subset
This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2994
- Accuracy: 0.9290
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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1200
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.1715 | 0.25 | 300 | 1.2094 | 0.5143 |
| 1.7378 | 1.25 | 600 | 0.7453 | 0.7429 |
| 0.4987 | 2.25 | 900 | 0.8685 | 0.8143 |
| 0.5731 | 3.25 | 1200 | 0.3579 | 0.8857 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.15.1
- Task
- Video Classification
- Library
- transformers
- Type
- videomae
- License
- cc-by-nc-4.0
- Created
- Jul 2, 2024
- Updated
- Jul 3, 2024