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# vit-base-patch16-224-food101

This model is a fine-tuned version of [eslamxm/vit-base-food101](https://huggingface.co/eslamxm/vit-base-food101) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3856
- Accuracy: 0.8971

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Script
```python
"cmd_list": [
        "python",
        "run_image_classification.py",
        "--model_name_or_path",
        "eslamxm/vit-base-food101",
        "--dataset_name",
        "food101",
        "--output_dir",
        "<output_dir>",
        "--overwrite_output_dir",
        "--remove_unused_columns",
        "False",
        "--do_train",
        "--do_eval",
        "--optim",
        "adamw_torch",
        "--learning_rate",
        "6e-05",
        "--num_train_epochs",
        "3",
        "--dataloader_num_workers",
        "10",
        "--per_device_train_batch_size",
        "64",
        "--gradient_accumulation_steps",
        "2",
        "--per_device_eval_batch_size",
        "128",
        "--logging_strategy",
        "steps",
        "--logging_steps",
        "10",
        "--evaluation_strategy",
        "steps",
        "--eval_steps",
        "500",
        "--save_steps",
        "500",
        "--evaluation_strategy",
        "epoch",
        "--save_strategy",
        "epoch",
        "--load_best_model_at_end",
        "False",
        "--save_total_limit",
        "1",
        "--seed",
        "42",
        "--fp16"
    ]
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3687        | 1.0   | 592  | 0.4044          | 0.8889   |
| 0.3422        | 2.0   | 1184 | 0.3911          | 0.8953   |
| 0.3808        | 3.0   | 1776 | 0.3856          | 0.8971   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1
- Datasets 2.11.0
- Tokenizers 0.13.3

apple_pie baby_back_ribs bruschetta waffles caesar_salad cannoli caprese_salad carrot_cake ceviche cheesecake cheese_plate chicken_curry chicken_quesadilla baklava chicken_wings chocolate_cake chocolate_mousse churros clam_chowder club_sandwich crab_cakes creme_brulee croque_madame cup_cakes beef_carpaccio deviled_eggs donuts dumplings edamame eggs_benedict escargots falafel filet_mignon fish_and_chips foie_gras beef_tartare french_fries french_onion_soup french_toast fried_calamari fried_rice frozen_yogurt garlic_bread gnocchi greek_salad grilled_cheese_sandwich beet_salad grilled_salmon guacamole gyoza hamburger hot_and_sour_soup hot_dog huevos_rancheros hummus ice_cream lasagna beignets lobster_bisque lobster_roll_sandwich macaroni_and_cheese macarons miso_soup mussels nachos omelette onion_rings oysters bibimbap pad_thai paella pancakes panna_cotta peking_duck pho pizza pork_chop poutine prime_rib bread_pudding pulled_pork_sandwich ramen ravioli red_velvet_cake risotto samosa sashimi scallops seaweed_salad shrimp_and_grits breakfast_burrito spaghetti_bolognese spaghetti_carbonara spring_rolls steak strawberry_shortcake sushi tacos takoyaki tiramisu tuna_tartare