JackWong0911 commited on
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Model save

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README.md CHANGED
@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/vivit-b-16x2-kinetics400](https://huggingface.co/google/vivit-b-16x2-kinetics400) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.1028
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- - Accuracy: 0.2759
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  ## Model description
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@@ -44,16 +44,22 @@ The following hyperparameters were used during training:
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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: 0.1
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- - training_steps: 1200
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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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- | 2.8108 | 0.25 | 300 | 2.4265 | 0.1622 |
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- | 2.5955 | 1.25 | 600 | 2.2181 | 0.1351 |
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- | 1.5744 | 2.25 | 900 | 2.3222 | 0.2973 |
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- | 1.9863 | 3.25 | 1200 | 2.1378 | 0.3514 |
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [google/vivit-b-16x2-kinetics400](https://huggingface.co/google/vivit-b-16x2-kinetics400) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.1644
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+ - Accuracy: 0.4138
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  ## Model description
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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: 0.1
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+ - training_steps: 3000
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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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+ | 3.2964 | 0.1 | 300 | 2.4749 | 0.1081 |
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+ | 3.1346 | 1.1 | 600 | 2.4779 | 0.1081 |
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+ | 1.9796 | 2.1 | 900 | 2.4339 | 0.2973 |
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+ | 1.8692 | 3.1 | 1200 | 2.1774 | 0.3243 |
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+ | 1.3617 | 4.1 | 1500 | 2.1613 | 0.3243 |
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+ | 2.0821 | 5.1 | 1800 | 1.9207 | 0.2162 |
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+ | 1.5534 | 6.1 | 2100 | 2.2003 | 0.3514 |
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+ | 0.5632 | 7.1 | 2400 | 3.0211 | 0.3514 |
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+ | 0.2806 | 8.1 | 2700 | 2.7735 | 0.3514 |
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+ | 1.3832 | 9.1 | 3000 | 2.9315 | 0.3514 |
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  ### Framework versions
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