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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 |
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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: 0.7220674282529953 |
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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 |
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This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the image_folder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7881 |
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- Accuracy: 0.7221 |
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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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| 1.2307 | 1.0 | 224 | 1.0863 | 0.5874 | |
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| 1.0893 | 2.0 | 448 | 0.9700 | 0.6362 | |
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| 1.0244 | 3.0 | 672 | 0.8859 | 0.6757 | |
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| 1.016 | 4.0 | 896 | 0.8804 | 0.6787 | |
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| 0.9089 | 5.0 | 1120 | 0.8611 | 0.6897 | |
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| 0.8935 | 6.0 | 1344 | 0.8283 | 0.7028 | |
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| 0.8403 | 7.0 | 1568 | 0.8116 | 0.7102 | |
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| 0.8179 | 8.0 | 1792 | 0.7934 | 0.7166 | |
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| 0.7764 | 9.0 | 2016 | 0.7865 | 0.7208 | |
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| 0.771 | 10.0 | 2240 | 0.7881 | 0.7221 | |
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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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