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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- food101 |
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metrics: |
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- accuracy |
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model-index: |
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- name: VIT-food101-image-classifier |
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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: food101 |
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type: food101 |
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config: default |
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split: train[:5000] |
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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: 0.933 |
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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-food101-image-classifier |
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This model was trained from scratch on the food101 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5661 |
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- Accuracy: 0.933 |
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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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| 1.1716 | 0.99 | 62 | 1.2149 | 0.896 | |
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| 0.7758 | 1.99 | 124 | 0.8727 | 0.906 | |
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| 0.6269 | 2.99 | 186 | 0.6833 | 0.928 | |
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| 0.5495 | 3.99 | 248 | 0.6041 | 0.931 | |
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| 0.4973 | 4.99 | 310 | 0.5661 | 0.933 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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