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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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- food101 |
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metrics: |
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- accuracy |
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model-index: |
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- name: my_food_model |
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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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# my_food_model |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4724 |
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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: 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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| 3.262 | 0.99 | 62 | 3.0234 | 0.775 | |
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| 1.8087 | 2.0 | 125 | 1.6821 | 0.853 | |
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| 1.2098 | 2.99 | 187 | 1.1582 | 0.91 | |
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| 0.8913 | 4.0 | 250 | 0.8991 | 0.92 | |
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| 0.7071 | 4.99 | 312 | 0.7349 | 0.912 | |
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| 0.5607 | 6.0 | 375 | 0.6197 | 0.921 | |
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| 0.4785 | 6.99 | 437 | 0.5506 | 0.929 | |
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| 0.3926 | 8.0 | 500 | 0.5015 | 0.93 | |
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| 0.3906 | 8.99 | 562 | 0.4902 | 0.927 | |
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| 0.3866 | 9.92 | 620 | 0.4724 | 0.933 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.0 |
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- Tokenizers 0.13.3 |
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