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vivit-b-16x2-kinetics400-finetuned-elder
minhah/vivit-b-16x2-kinetics400-finetuned-elder
vivit-b-16x2-kinetics400-finetuned-elder is a video classification model from minhah. 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
- Mar 19, 2024
- Updated
- Mar 19, 2024
- SHA
- f96c0b88
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-b-16x2-kinetics400-finetuned-elder
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: 1.6807
- Accuracy: 0.3205
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: 8
- eval_batch_size: 8
- 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: 576
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.6183 | 0.25 | 145 | 1.6139 | 0.3360 |
| 1.5777 | 1.25 | 290 | 1.6061 | 0.3024 |
| 1.36 | 2.25 | 435 | 1.6442 | 0.2863 |
| 1.5395 | 3.24 | 576 | 1.6518 | 0.2688 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Video Classification
- Library
- transformers
- Type
- vivit
- License
- mit
- Created
- Mar 19, 2024
- Updated
- Mar 19, 2024