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vit-Facial-Expression-Recognition

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facial-expression-recognition-ferplus

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  1. README.md +20 -17
  2. all_results.json +6 -6
  3. eval_results.json +6 -6
  4. model.safetensors +1 -1
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 [carlosleao/vit-Facial-Expression-Recognition](https://huggingface.co/carlosleao/vit-Facial-Expression-Recognition) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.5324
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- - Accuracy: 0.4661
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  ## Model description
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@@ -46,28 +46,31 @@ 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: cosine
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  - lr_scheduler_warmup_steps: 1000
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- - num_epochs: 10
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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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- | 1.8118 | 0.8959 | 100 | 1.7186 | 0.3764 |
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- | 1.6033 | 1.7917 | 200 | 1.5911 | 0.4088 |
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- | 1.5483 | 2.6876 | 300 | 1.5577 | 0.4043 |
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- | 1.516 | 3.5834 | 400 | 1.5156 | 0.4476 |
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- | 1.4477 | 4.4793 | 500 | 1.4922 | 0.4504 |
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- | 1.406 | 5.3751 | 600 | 1.4979 | 0.4409 |
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- | 1.3453 | 6.2710 | 700 | 1.4768 | 0.4624 |
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- | 1.268 | 7.1669 | 800 | 1.4816 | 0.4602 |
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- | 1.2326 | 8.0627 | 900 | 1.6040 | 0.4127 |
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- | 1.1273 | 8.9586 | 1000 | 1.5324 | 0.4661 |
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- | 0.937 | 9.8544 | 1100 | 1.6156 | 0.4367 |
 
 
 
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  ### Framework versions
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  - Transformers 4.45.2
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  - Pytorch 2.5.0+cu124
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- - Datasets 3.0.1
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  - Tokenizers 0.20.1
 
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  This model is a fine-tuned version of [carlosleao/vit-Facial-Expression-Recognition](https://huggingface.co/carlosleao/vit-Facial-Expression-Recognition) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.5831
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+ - Accuracy: 0.4234
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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: cosine
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  - lr_scheduler_warmup_steps: 1000
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+ - num_epochs: 30
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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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+ | 1.7711 | 2.0833 | 100 | 1.7038 | 0.3915 |
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+ | 1.5964 | 4.1667 | 200 | 1.5836 | 0.3957 |
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+ | 1.5002 | 6.25 | 300 | 1.5438 | 0.4113 |
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+ | 1.3591 | 8.3333 | 400 | 1.5690 | 0.4016 |
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+ | 1.1821 | 10.4167 | 500 | 1.5831 | 0.4234 |
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+ | 0.9323 | 12.5 | 600 | 1.7256 | 0.3960 |
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+ | 0.7237 | 14.5833 | 700 | 1.8623 | 0.3954 |
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+ | 0.5376 | 16.6667 | 800 | 2.0238 | 0.3742 |
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+ | 0.363 | 18.75 | 900 | 2.1839 | 0.3462 |
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+ | 0.238 | 20.8333 | 1000 | 2.2616 | 0.3602 |
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+ | 0.1686 | 22.9167 | 1100 | 2.3515 | 0.3758 |
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+ | 0.0952 | 25.0 | 1200 | 2.4730 | 0.3836 |
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+ | 0.0526 | 27.0833 | 1300 | 2.5531 | 0.3898 |
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+ | 0.0399 | 29.1667 | 1400 | 2.5901 | 0.3778 |
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  ### Framework versions
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  - Transformers 4.45.2
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  - Pytorch 2.5.0+cu124
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+ - Datasets 3.0.2
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  - Tokenizers 0.20.1
all_results.json CHANGED
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  {
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- "epoch": 9.944008958566629,
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- "eval_accuracy": 0.4660519698239732,
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- "eval_loss": 1.5323501825332642,
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- "eval_runtime": 25.4534,
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- "eval_samples_per_second": 140.61,
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- "eval_steps_per_second": 4.4
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  }
 
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  {
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+ "epoch": 30.0,
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+ "eval_accuracy": 0.42340286831812257,
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+ "eval_loss": 1.5831071138381958,
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+ "eval_runtime": 26.0496,
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+ "eval_samples_per_second": 117.775,
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+ "eval_steps_per_second": 3.685
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  }
eval_results.json CHANGED
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- "eval_accuracy": 0.4660519698239732,
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- "eval_loss": 1.5323501825332642,
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- "eval_runtime": 25.4534,
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- "eval_samples_per_second": 140.61,
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- "eval_steps_per_second": 4.4
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  }
 
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+ "eval_loss": 1.5831071138381958,
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+ "eval_runtime": 26.0496,
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+ "eval_samples_per_second": 117.775,
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+ "eval_steps_per_second": 3.685
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  }
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