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Thao2202
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vit-Facial-Expression-Recognition

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Inference Endpoints
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End of training

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  1. README.md +23 -17
  2. all_results.json +8 -5
  3. eval_results.json +8 -5
README.md CHANGED
@@ -5,6 +5,9 @@ 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: vit-Facial-Expression-Recognition
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  results: []
@@ -17,8 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [motheecreator/vit-Facial-Expression-Recognition](https://huggingface.co/motheecreator/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: 0.3705
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- - Accuracy: 0.8735
 
 
 
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  ## Model description
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@@ -50,21 +56,21 @@ The following hyperparameters were used during training:
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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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- | 4.5195 | 0.2164 | 100 | 0.3776 | 0.8729 |
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- | 4.5328 | 0.4328 | 200 | 0.3786 | 0.8718 |
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- | 4.554 | 0.6492 | 300 | 0.3800 | 0.8717 |
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- | 4.5812 | 0.8656 | 400 | 0.3764 | 0.8739 |
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- | 4.2724 | 1.0801 | 500 | 0.3793 | 0.8722 |
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- | 4.5232 | 1.2965 | 600 | 0.3833 | 0.8693 |
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- | 4.4717 | 1.5128 | 700 | 0.3864 | 0.8684 |
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- | 4.4636 | 1.7292 | 800 | 0.3875 | 0.8676 |
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- | 4.5234 | 1.9456 | 900 | 0.3897 | 0.8667 |
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- | 4.2156 | 2.1601 | 1000 | 0.3993 | 0.8632 |
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- | 4.063 | 2.3765 | 1100 | 0.3934 | 0.8651 |
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- | 4.1068 | 2.5929 | 1200 | 0.3823 | 0.8702 |
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- | 3.9902 | 2.8093 | 1300 | 0.3724 | 0.8734 |
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  ### Framework versions
 
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  - generated_from_trainer
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  metrics:
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  - accuracy
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+ - f1
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+ - precision
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+ - recall
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  model-index:
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  - name: vit-Facial-Expression-Recognition
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  results: []
 
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  This model is a fine-tuned version of [motheecreator/vit-Facial-Expression-Recognition](https://huggingface.co/motheecreator/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: 0.3658
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+ - Accuracy: 0.8753
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+ - F1: 0.8737
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+ - Precision: 0.8749
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+ - Recall: 0.8753
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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+ |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 4.5618 | 0.2164 | 100 | 0.3710 | 0.8762 | 0.8746 | 0.8752 | 0.8762 |
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+ | 4.6091 | 0.4328 | 200 | 0.3677 | 0.8761 | 0.8747 | 0.8762 | 0.8761 |
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+ | 4.5423 | 0.6492 | 300 | 0.3695 | 0.8748 | 0.8730 | 0.8745 | 0.8748 |
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+ | 4.6307 | 0.8656 | 400 | 0.3745 | 0.8711 | 0.8692 | 0.8730 | 0.8711 |
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+ | 4.3953 | 1.0801 | 500 | 0.3745 | 0.8727 | 0.8711 | 0.8724 | 0.8727 |
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+ | 4.341 | 1.2965 | 600 | 0.3803 | 0.8688 | 0.8674 | 0.8688 | 0.8688 |
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+ | 4.5471 | 1.5128 | 700 | 0.3841 | 0.8713 | 0.8699 | 0.8710 | 0.8713 |
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+ | 4.522 | 1.7292 | 800 | 0.3836 | 0.8679 | 0.8662 | 0.8678 | 0.8679 |
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+ | 4.5596 | 1.9456 | 900 | 0.3885 | 0.8672 | 0.8649 | 0.8678 | 0.8672 |
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+ | 4.1491 | 2.1601 | 1000 | 0.3849 | 0.8691 | 0.8677 | 0.8689 | 0.8691 |
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+ | 4.1037 | 2.3765 | 1100 | 0.3906 | 0.8667 | 0.8647 | 0.8669 | 0.8667 |
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+ | 4.0033 | 2.5929 | 1200 | 0.3784 | 0.8704 | 0.8687 | 0.8699 | 0.8704 |
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+ | 3.9759 | 2.8093 | 1300 | 0.3677 | 0.8752 | 0.8737 | 0.8747 | 0.8752 |
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  ### Framework versions
all_results.json CHANGED
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  {
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  "epoch": 2.995401677035434,
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- "eval_accuracy": 0.8734528238079134,
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- "eval_loss": 0.3704567551612854,
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- "eval_runtime": 362.8359,
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- "eval_samples_per_second": 81.497,
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- "eval_steps_per_second": 2.549
 
 
 
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  }
 
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  {
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  "epoch": 2.995401677035434,
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+ "eval_accuracy": 0.8752789989854582,
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+ "eval_f1": 0.8737068810871955,
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+ "eval_loss": 0.36578133702278137,
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+ "eval_precision": 0.8748779898220497,
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+ "eval_recall": 0.8752789989854582,
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+ "eval_runtime": 349.2694,
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+ "eval_samples_per_second": 84.662,
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+ "eval_steps_per_second": 2.648
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  }
eval_results.json CHANGED
@@ -1,8 +1,11 @@
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  {
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  "epoch": 2.995401677035434,
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- "eval_accuracy": 0.8734528238079134,
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- "eval_loss": 0.3704567551612854,
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- "eval_runtime": 362.8359,
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- "eval_samples_per_second": 81.497,
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- "eval_steps_per_second": 2.549
 
 
 
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  }
 
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  {
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  "epoch": 2.995401677035434,
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+ "eval_accuracy": 0.8752789989854582,
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+ "eval_f1": 0.8737068810871955,
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+ "eval_loss": 0.36578133702278137,
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+ "eval_precision": 0.8748779898220497,
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+ "eval_recall": 0.8752789989854582,
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+ "eval_runtime": 349.2694,
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+ "eval_samples_per_second": 84.662,
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+ "eval_steps_per_second": 2.648
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  }