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---
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license: cc-by-nc-4.0
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base_model: MCG-NJU/videomae-base-finetuned-kinetics
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: videomae-finetuned
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# videomae-finetuned
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This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1289
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- Accuracy: 0.9706
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.2
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- training_steps: 8620
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.7657 | 0.05 | 432 | 0.6104 | 0.8243 |
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| 0.4011 | 1.05 | 864 | 0.4283 | 0.8896 |
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| 0.301 | 2.05 | 1296 | 0.3957 | 0.8833 |
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| 0.2967 | 3.05 | 1728 | 0.3066 | 0.9138 |
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| 0.229 | 4.05 | 2160 | 0.3268 | 0.9092 |
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| 0.2185 | 5.05 | 2592 | 0.2865 | 0.9281 |
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| 0.1863 | 6.05 | 3024 | 0.2550 | 0.9320 |
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| 0.1386 | 7.05 | 3456 | 0.2366 | 0.9422 |
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| 0.1542 | 8.05 | 3888 | 0.2162 | 0.9449 |
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| 0.1396 | 9.05 | 4320 | 0.2069 | 0.9424 |
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| 0.1323 | 10.05 | 4752 | 0.2021 | 0.9486 |
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| 0.1008 | 11.05 | 5184 | 0.2408 | 0.9465 |
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| 0.0756 | 12.05 | 5616 | 0.2023 | 0.9509 |
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| 0.0628 | 13.05 | 6048 | 0.1730 | 0.9609 |
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| 0.0747 | 14.05 | 6480 | 0.1747 | 0.9622 |
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| 0.047 | 15.05 | 6912 | 0.1698 | 0.9629 |
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| 0.0586 | 16.05 | 7344 | 0.1412 | 0.9668 |
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| 0.0644 | 17.05 | 7776 | 0.1314 | 0.9704 |
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| 0.0521 | 18.05 | 8208 | 0.1343 | 0.9688 |
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| 0.0516 | 19.05 | 8620 | 0.1289 | 0.9706 |
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### Framework versions
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- Transformers 4.38.1
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- Pytorch 2.0.1+cu118
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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