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--- |
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base_model: nateraw/vit-age-classifier |
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tags: |
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- generated_from_trainer |
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datasets: |
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- fair_face |
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metrics: |
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- accuracy |
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model-index: |
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- name: image_age_classification |
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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: fair_face |
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type: fair_face |
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config: '0.25' |
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split: train[:10000] |
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args: '0.25' |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.601 |
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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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# image_age_classification |
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This model is a fine-tuned version of [nateraw/vit-age-classifier](https://huggingface.co/nateraw/vit-age-classifier) on the fair_face dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9464 |
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- Accuracy: 0.601 |
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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: 5e-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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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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: 5 |
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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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| 0.9107 | 1.0 | 125 | 0.9360 | 0.6065 | |
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| 0.7945 | 2.0 | 250 | 0.9545 | 0.588 | |
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| 1.0256 | 3.0 | 375 | 1.0144 | 0.586 | |
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| 0.7354 | 4.0 | 500 | 0.9726 | 0.594 | |
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| 0.6979 | 5.0 | 625 | 0.9735 | 0.5995 | |
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### Framework versions |
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- Transformers 4.34.0.dev0 |
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- Pytorch 1.12.1+cu116 |
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- Datasets 2.14.5 |
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- Tokenizers 0.12.1 |
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