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krismp
/
emotion_recognition

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Model card Files Community
3
krismp commited on
Commit
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1 Parent(s): 5b90f47

End of training

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Files changed (3) hide show
  1. README.md +18 -18
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.15625
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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
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.0560
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- - Accuracy: 0.1562
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 10 | 2.0641 | 0.1562 |
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- | No log | 2.0 | 20 | 1.9868 | 0.1562 |
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- | No log | 3.0 | 30 | 1.8525 | 0.1562 |
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- | No log | 4.0 | 40 | 1.7080 | 0.1562 |
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- | No log | 5.0 | 50 | 1.5731 | 0.1375 |
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- | No log | 6.0 | 60 | 1.5035 | 0.1 |
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- | No log | 7.0 | 70 | 1.4341 | 0.0625 |
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- | No log | 8.0 | 80 | 1.3996 | 0.0688 |
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- | No log | 9.0 | 90 | 1.3553 | 0.1062 |
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- | No log | 10.0 | 100 | 1.3415 | 0.1125 |
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- | No log | 11.0 | 110 | 1.3121 | 0.1 |
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- | No log | 12.0 | 120 | 1.2617 | 0.1 |
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- | No log | 13.0 | 130 | 1.2658 | 0.1062 |
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- | No log | 14.0 | 140 | 1.3010 | 0.1125 |
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- | No log | 15.0 | 150 | 1.2777 | 0.1125 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.1625
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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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  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.6239
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+ - Accuracy: 0.1625
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 10 | 2.0644 | 0.1562 |
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+ | No log | 2.0 | 20 | 1.9775 | 0.1562 |
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+ | No log | 3.0 | 30 | 1.8552 | 0.1562 |
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+ | No log | 4.0 | 40 | 1.7118 | 0.1562 |
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+ | No log | 5.0 | 50 | 1.6050 | 0.1625 |
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+ | No log | 6.0 | 60 | 1.5192 | 0.1313 |
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+ | No log | 7.0 | 70 | 1.4569 | 0.0875 |
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+ | No log | 8.0 | 80 | 1.4168 | 0.0875 |
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+ | No log | 9.0 | 90 | 1.3718 | 0.1 |
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+ | No log | 10.0 | 100 | 1.3739 | 0.1125 |
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+ | No log | 11.0 | 110 | 1.3336 | 0.1 |
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+ | No log | 12.0 | 120 | 1.3096 | 0.1187 |
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+ | No log | 13.0 | 130 | 1.3114 | 0.1125 |
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+ | No log | 14.0 | 140 | 1.3268 | 0.1062 |
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+ | No log | 15.0 | 150 | 1.2846 | 0.1062 |
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  ### Framework versions
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