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hansin91
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emotion_classification

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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  1. README.md +34 -34
  2. pytorch_model.bin +1 -1
README.md CHANGED
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.58125
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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: 2.0973
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- - Accuracy: 0.5813
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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: 4e-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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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.0283 | 0.97 | 13 | 2.0768 | 0.575 |
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- | 0.0243 | 1.95 | 26 | 2.1315 | 0.5437 |
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- | 0.0174 | 3.0 | 40 | 2.1721 | 0.5437 |
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- | 0.0176 | 3.98 | 53 | 2.1687 | 0.525 |
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- | 0.0172 | 4.95 | 66 | 2.0882 | 0.5375 |
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- | 0.016 | 6.0 | 80 | 1.9676 | 0.5687 |
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- | 0.017 | 6.97 | 93 | 2.1717 | 0.5375 |
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- | 0.0169 | 7.95 | 106 | 2.0982 | 0.5375 |
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- | 0.0157 | 9.0 | 120 | 2.0893 | 0.5563 |
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- | 0.0169 | 9.97 | 133 | 1.7681 | 0.6188 |
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- | 0.0179 | 10.95 | 146 | 2.1032 | 0.5437 |
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- | 0.0186 | 12.0 | 160 | 2.1116 | 0.55 |
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- | 0.0196 | 12.97 | 173 | 2.2203 | 0.5625 |
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- | 0.018 | 13.95 | 186 | 2.0634 | 0.575 |
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- | 0.0193 | 15.0 | 200 | 2.2562 | 0.5312 |
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- | 0.026 | 15.97 | 213 | 2.3150 | 0.5062 |
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- | 0.0171 | 16.95 | 226 | 2.0457 | 0.5437 |
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- | 0.0194 | 18.0 | 240 | 1.9336 | 0.5938 |
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- | 0.0657 | 18.98 | 253 | 2.1706 | 0.5188 |
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- | 0.0537 | 19.95 | 266 | 2.0839 | 0.5437 |
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- | 0.0566 | 21.0 | 280 | 2.3004 | 0.4813 |
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- | 0.074 | 21.98 | 293 | 2.1488 | 0.5375 |
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- | 0.0394 | 22.95 | 306 | 2.4144 | 0.475 |
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- | 0.0228 | 24.0 | 320 | 2.2085 | 0.55 |
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- | 0.0514 | 24.98 | 333 | 2.1443 | 0.5312 |
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- | 0.0553 | 25.95 | 346 | 2.3013 | 0.5062 |
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- | 0.0436 | 27.0 | 360 | 1.9988 | 0.5813 |
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- | 0.1468 | 27.98 | 373 | 2.0166 | 0.5563 |
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- | 0.2184 | 28.95 | 386 | 2.4145 | 0.5 |
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- | 0.1519 | 29.25 | 390 | 2.2032 | 0.5375 |
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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: 0.575
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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: 1.2677
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+ - Accuracy: 0.575
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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: 3e-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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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.9379 | 0.97 | 13 | 1.2947 | 0.4875 |
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+ | 0.9235 | 1.95 | 26 | 1.3397 | 0.475 |
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+ | 0.8298 | 3.0 | 40 | 1.2971 | 0.5563 |
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+ | 0.8883 | 3.98 | 53 | 1.3434 | 0.4875 |
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+ | 0.8547 | 4.95 | 66 | 1.3226 | 0.475 |
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+ | 0.8129 | 6.0 | 80 | 1.3077 | 0.5062 |
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+ | 0.8095 | 6.97 | 93 | 1.2503 | 0.525 |
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+ | 0.7764 | 7.95 | 106 | 1.2989 | 0.5312 |
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+ | 0.7004 | 9.0 | 120 | 1.3383 | 0.4813 |
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+ | 0.7013 | 9.97 | 133 | 1.3370 | 0.5125 |
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+ | 0.6416 | 10.95 | 146 | 1.3073 | 0.5125 |
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+ | 0.5831 | 12.0 | 160 | 1.3192 | 0.5 |
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+ | 0.5968 | 12.97 | 173 | 1.2394 | 0.5375 |
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+ | 0.5434 | 13.95 | 186 | 1.3389 | 0.5188 |
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+ | 0.4605 | 15.0 | 200 | 1.2951 | 0.525 |
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+ | 0.4674 | 15.97 | 213 | 1.2038 | 0.5687 |
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+ | 0.3953 | 16.95 | 226 | 1.4019 | 0.5062 |
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+ | 0.3595 | 18.0 | 240 | 1.4442 | 0.4813 |
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+ | 0.3619 | 18.98 | 253 | 1.4213 | 0.525 |
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+ | 0.3304 | 19.95 | 266 | 1.2937 | 0.5437 |
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+ | 0.34 | 21.0 | 280 | 1.3024 | 0.5687 |
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+ | 0.4215 | 21.98 | 293 | 1.4018 | 0.5375 |
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+ | 0.3606 | 22.95 | 306 | 1.4221 | 0.5375 |
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+ | 0.3402 | 24.0 | 320 | 1.4987 | 0.4313 |
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+ | 0.3058 | 24.98 | 333 | 1.5120 | 0.5125 |
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+ | 0.3047 | 25.95 | 346 | 1.5749 | 0.5 |
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+ | 0.3616 | 27.0 | 360 | 1.4293 | 0.5188 |
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+ | 0.3315 | 27.98 | 373 | 1.5326 | 0.5312 |
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+ | 0.3535 | 28.95 | 386 | 1.5095 | 0.5188 |
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+ | 0.3056 | 29.25 | 390 | 1.5366 | 0.5 |
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  ### Framework versions
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