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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: 0.5259515570934256 |
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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: 1.5659 |
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- Accuracy: 0.5260 |
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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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