| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - food101 |
| metrics: |
| - accuracy |
| model-index: |
| - name: foods |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: food101 |
| type: food101 |
| config: default |
| split: train[:5000] |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.925 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # foods |
|
|
| 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. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.4850 |
| - Accuracy: 0.925 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 64 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 10 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 3.215 | 0.99 | 62 | 2.9381 | 0.778 | |
| | 1.7683 | 2.0 | 125 | 1.6041 | 0.911 | |
| | 1.2081 | 2.99 | 187 | 1.1491 | 0.894 | |
| | 0.82 | 4.0 | 250 | 0.9028 | 0.899 | |
| | 0.7188 | 4.99 | 312 | 0.7217 | 0.913 | |
| | 0.5186 | 6.0 | 375 | 0.5988 | 0.928 | |
| | 0.4582 | 6.99 | 437 | 0.5468 | 0.926 | |
| | 0.4185 | 8.0 | 500 | 0.4943 | 0.93 | |
| | 0.3909 | 8.99 | 562 | 0.4865 | 0.925 | |
| | 0.3513 | 9.92 | 620 | 0.4850 | 0.925 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.28.0 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.13.0 |
| - Tokenizers 0.13.3 |
|
|
