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videomae-base-finetuned-ucf101-subset
ali5341/videomae-base-finetuned-ucf101-subset
videomae-base-finetuned-ucf101-subset is a video classification model from ali5341. 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
- Oct 5, 2023
- Updated
- Oct 10, 2023
- SHA
- 48173624
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: 1.6506
- Accuracy: 0.5587
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 29760
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 4.1471 | 0.2 | 5952 | 4.1601 | 0.0382 |
| 4.0729 | 1.2 | 11904 | 3.6134 | 0.1013 |
| 2.6787 | 2.2 | 17856 | 2.9397 | 0.2193 |
| 1.722 | 3.2 | 23808 | 2.0974 | 0.4241 |
| 0.6968 | 4.2 | 29760 | 1.7135 | 0.5362 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
- Task
- Video Classification
- Library
- transformers
- Type
- videomae
- License
- cc-by-nc-4.0
- Created
- Oct 5, 2023
- Updated
- Oct 10, 2023