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# my_food_model

This model is a fine-tuned version of [apple/mobilevit-small](https://huggingface.co/apple/mobilevit-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0492
- Accuracy: 0.7236

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 4.4987        | 1.0   | 592   | 4.4835          | 0.1002   |
| 3.6382        | 2.0   | 1184  | 3.4318          | 0.2707   |
| 2.9497        | 3.0   | 1776  | 2.5897          | 0.3945   |
| 2.586         | 4.0   | 2368  | 2.1261          | 0.4824   |
| 2.2806        | 5.0   | 2960  | 1.8201          | 0.5501   |
| 2.0928        | 6.0   | 3552  | 1.6291          | 0.5896   |
| 1.9839        | 7.0   | 4144  | 1.4954          | 0.6145   |
| 1.8465        | 8.0   | 4736  | 1.4209          | 0.6333   |
| 1.6939        | 9.0   | 5328  | 1.3486          | 0.6493   |
| 1.6212        | 10.0  | 5920  | 1.2959          | 0.6616   |
| 1.6672        | 11.0  | 6512  | 1.2299          | 0.6744   |
| 1.5973        | 12.0  | 7104  | 1.2018          | 0.6871   |
| 1.5419        | 13.0  | 7696  | 1.1750          | 0.6928   |
| 1.5003        | 14.0  | 8288  | 1.1297          | 0.7017   |
| 1.4908        | 15.0  | 8880  | 1.1184          | 0.7030   |
| 1.4033        | 16.0  | 9472  | 1.0983          | 0.7125   |
| 1.4015        | 17.0  | 10064 | 1.0832          | 0.7159   |
| 1.3651        | 18.0  | 10656 | 1.0728          | 0.7134   |
| 1.3698        | 19.0  | 11248 | 1.0678          | 0.7166   |
| 1.4136        | 20.0  | 11840 | 1.0541          | 0.7217   |
| 1.4679        | 21.0  | 12432 | 1.0542          | 0.7208   |
| 1.3328        | 22.0  | 13024 | 1.0466          | 0.7253   |
| 1.2773        | 23.0  | 13616 | 1.0655          | 0.7188   |
| 1.342         | 24.0  | 14208 | 1.0471          | 0.7236   |
| 1.3437        | 25.0  | 14800 | 1.0492          | 0.7236   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0

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