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--- |
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base_model: motheecreator/vit-Facial-Expression-Recognition |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-Facial-Expression-Recognition |
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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: imagefolder |
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type: imagefolder |
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config: default |
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split: None |
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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: 0.9148438153091649 |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: image-classification |
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library_name: transformers |
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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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# vit-Facial-Expression-Recognition |
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This model is a fine-tuned version of [motheecreator/vit-Facial-Expression-Recognition](https://huggingface.co/motheecreator/vit-Facial-Expression-Recognition) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2606 |
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- Accuracy: 0.9148 |
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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: 3e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 512 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 3 |
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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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| 0.6309 | 0.3328 | 100 | 0.2618 | 0.9145 | |
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| 0.6165 | 0.6656 | 200 | 0.2600 | 0.9150 | |
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| 0.6283 | 0.9983 | 300 | 0.2659 | 0.9135 | |
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| 0.6171 | 1.3311 | 400 | 0.2561 | 0.9174 | |
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| 0.6112 | 1.6639 | 500 | 0.2606 | 0.9148 | |
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| 0.6081 | 1.9967 | 600 | 0.2624 | 0.9137 | |
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| 0.5885 | 2.3295 | 700 | 0.2671 | 0.9113 | |
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| 0.5975 | 2.6622 | 800 | 0.2572 | 0.9156 | |
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| 0.6067 | 2.9950 | 900 | 0.2683 | 0.9116 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
