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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- image_folder
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metrics:
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- accuracy
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model-index:
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- name: beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05-finetuned-FER2013-7e-05
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: image_folder
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type: image_folder
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.49596309111880044
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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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should probably proofread and complete it, then remove this comment. -->
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# beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05-finetuned-FER2013-7e-05
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This model is a fine-tuned version of [lixiqi/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05](https://huggingface.co/lixiqi/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05) on the image_folder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.6041
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- Accuracy: 0.4960
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 7e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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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: 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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| 1.6537 | 0.97 | 14 | 1.4980 | 0.4683 |
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| 1.4325 | 1.97 | 28 | 1.4777 | 0.5040 |
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| 1.1532 | 2.97 | 42 | 1.5007 | 0.4960 |
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| 1.0428 | 3.97 | 56 | 1.5480 | 0.4890 |
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| 0.8716 | 4.97 | 70 | 1.5659 | 0.5260 |
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| 0.892 | 5.97 | 84 | 1.6132 | 0.4960 |
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| 0.8109 | 6.97 | 98 | 1.5895 | 0.5167 |
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| 0.7413 | 7.97 | 112 | 1.6271 | 0.5202 |
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| 0.765 | 8.97 | 126 | 1.5991 | 0.5040 |
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| 0.6575 | 9.97 | 140 | 1.6041 | 0.4960 |
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### Framework versions
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- Transformers 4.20.1
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- Pytorch 1.11.0
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- Datasets 2.1.0
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- Tokenizers 0.12.1
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