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emotion_recognition

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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End of training

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Files changed (3) hide show
  1. README.md +55 -23
  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: 0.1625
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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
@@ -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: 1.6239
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- - Accuracy: 0.1625
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  ## Model description
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@@ -52,36 +52,68 @@ More information needed
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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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- - gradient_accumulation_steps: 4
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- - total_train_batch_size: 64
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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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- - num_epochs: 15
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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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- | 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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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.125
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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: 2.0993
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+ - Accuracy: 0.125
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.0005
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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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+ - num_epochs: 50
 
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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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+ | No log | 1.0 | 40 | 2.0986 | 0.125 |
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+ | No log | 2.0 | 80 | 2.0816 | 0.125 |
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+ | No log | 3.0 | 120 | 2.0798 | 0.125 |
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+ | No log | 4.0 | 160 | 2.0765 | 0.125 |
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+ | No log | 5.0 | 200 | 2.0765 | 0.125 |
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+ | No log | 6.0 | 240 | 2.0820 | 0.125 |
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+ | No log | 7.0 | 280 | 2.0796 | 0.125 |
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+ | No log | 8.0 | 320 | 2.0826 | 0.125 |
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+ | No log | 9.0 | 360 | 2.0759 | 0.125 |
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+ | No log | 10.0 | 400 | 2.0799 | 0.125 |
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+ | No log | 11.0 | 440 | 2.0593 | 0.125 |
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+ | No log | 12.0 | 480 | 2.0813 | 0.125 |
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+ | 2.0843 | 13.0 | 520 | 2.0817 | 0.125 |
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+ | 2.0843 | 14.0 | 560 | 2.1626 | 0.125 |
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+ | 2.0843 | 15.0 | 600 | 2.1105 | 0.125 |
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+ | 2.0843 | 16.0 | 640 | 2.0921 | 0.125 |
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+ | 2.0843 | 17.0 | 680 | 2.0878 | 0.125 |
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+ | 2.0843 | 18.0 | 720 | 2.0877 | 0.125 |
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+ | 2.0843 | 19.0 | 760 | 2.0815 | 0.125 |
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+ | 2.0843 | 20.0 | 800 | 2.0812 | 0.125 |
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+ | 2.0843 | 21.0 | 840 | 2.0810 | 0.125 |
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+ | 2.0843 | 22.0 | 880 | 2.0796 | 0.125 |
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+ | 2.0843 | 23.0 | 920 | 2.0798 | 0.125 |
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+ | 2.0843 | 24.0 | 960 | 2.0808 | 0.125 |
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+ | 2.0948 | 25.0 | 1000 | 2.0812 | 0.125 |
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+ | 2.0948 | 26.0 | 1040 | 2.0806 | 0.125 |
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+ | 2.0948 | 27.0 | 1080 | 2.0797 | 0.125 |
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+ | 2.0948 | 28.0 | 1120 | 2.0795 | 0.125 |
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+ | 2.0948 | 29.0 | 1160 | 2.0801 | 0.125 |
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+ | 2.0948 | 30.0 | 1200 | 2.0792 | 0.125 |
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+ | 2.0948 | 31.0 | 1240 | 2.0783 | 0.125 |
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+ | 2.0948 | 32.0 | 1280 | 2.0792 | 0.125 |
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+ | 2.0948 | 33.0 | 1320 | 2.0786 | 0.125 |
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+ | 2.0948 | 34.0 | 1360 | 2.0769 | 0.125 |
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+ | 2.0948 | 35.0 | 1400 | 2.0686 | 0.125 |
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+ | 2.0948 | 36.0 | 1440 | 2.0616 | 0.125 |
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+ | 2.0948 | 37.0 | 1480 | 2.0653 | 0.125 |
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+ | 2.0804 | 38.0 | 1520 | 2.0970 | 0.125 |
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+ | 2.0804 | 39.0 | 1560 | 2.0815 | 0.125 |
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+ | 2.0804 | 40.0 | 1600 | 2.0743 | 0.125 |
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+ | 2.0804 | 41.0 | 1640 | 2.0802 | 0.125 |
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+ | 2.0804 | 42.0 | 1680 | 2.0655 | 0.125 |
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+ | 2.0804 | 43.0 | 1720 | 2.0768 | 0.125 |
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+ | 2.0804 | 44.0 | 1760 | 2.0642 | 0.125 |
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+ | 2.0804 | 45.0 | 1800 | 2.0637 | 0.125 |
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+ | 2.0804 | 46.0 | 1840 | 2.0687 | 0.125 |
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+ | 2.0804 | 47.0 | 1880 | 2.0603 | 0.125 |
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+ | 2.0804 | 48.0 | 1920 | 2.0507 | 0.125 |
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+ | 2.0804 | 49.0 | 1960 | 2.0395 | 0.125 |
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+ | 2.0589 | 50.0 | 2000 | 2.0600 | 0.125 |
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  ### Framework versions
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