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# my_awesome_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: 0.5132
- Accuracy: 0.8636

## 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: 100

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 4.614         | 0.9829  | 43   | 4.6167          | 0.01     |
| 4.5907        | 1.9886  | 87   | 4.5880          | 0.0214   |
| 4.5382        | 2.9943  | 131  | 4.5343          | 0.105    |
| 4.4551        | 4.0     | 175  | 4.4235          | 0.3043   |
| 4.287         | 4.9829  | 218  | 4.2081          | 0.5043   |
| 3.885         | 5.9886  | 262  | 3.8133          | 0.5307   |
| 3.4412        | 6.9943  | 306  | 3.3131          | 0.4907   |
| 2.8825        | 8.0     | 350  | 2.8045          | 0.4871   |
| 2.5521        | 8.9829  | 393  | 2.3834          | 0.4671   |
| 2.2911        | 9.9886  | 437  | 2.0563          | 0.5186   |
| 2.0126        | 10.9943 | 481  | 1.8029          | 0.5679   |
| 1.8294        | 12.0    | 525  | 1.6611          | 0.605    |
| 1.745         | 12.9829 | 568  | 1.4988          | 0.6257   |
| 1.6162        | 13.9886 | 612  | 1.3868          | 0.62     |
| 1.5084        | 14.9943 | 656  | 1.2984          | 0.6429   |
| 1.4441        | 16.0    | 700  | 1.2081          | 0.6457   |
| 1.3625        | 16.9829 | 743  | 1.1554          | 0.6814   |
| 1.2752        | 17.9886 | 787  | 1.0955          | 0.6929   |
| 1.224         | 18.9943 | 831  | 1.0373          | 0.7164   |
| 1.2096        | 20.0    | 875  | 1.0375          | 0.7164   |
| 1.1551        | 20.9829 | 918  | 0.9842          | 0.7414   |
| 1.1079        | 21.9886 | 962  | 0.9645          | 0.7571   |
| 1.0669        | 22.9943 | 1006 | 0.9150          | 0.77     |
| 1.0206        | 24.0    | 1050 | 0.8508          | 0.7836   |
| 0.9963        | 24.9829 | 1093 | 0.8458          | 0.7743   |
| 0.9132        | 25.9886 | 1137 | 0.7838          | 0.7971   |
| 0.863         | 26.9943 | 1181 | 0.7590          | 0.8057   |
| 0.8669        | 28.0    | 1225 | 0.7646          | 0.785    |
| 0.8776        | 28.9829 | 1268 | 0.7084          | 0.8157   |
| 0.793         | 29.9886 | 1312 | 0.6862          | 0.82     |
| 0.7941        | 30.9943 | 1356 | 0.6971          | 0.8143   |
| 0.7863        | 32.0    | 1400 | 0.6135          | 0.8314   |
| 0.7344        | 32.9829 | 1443 | 0.5961          | 0.8407   |
| 0.6888        | 33.9886 | 1487 | 0.6304          | 0.845    |
| 0.6693        | 34.9943 | 1531 | 0.6011          | 0.8364   |
| 0.6736        | 36.0    | 1575 | 0.5917          | 0.8364   |
| 0.6739        | 36.9829 | 1618 | 0.5933          | 0.8336   |
| 0.6595        | 37.9886 | 1662 | 0.5824          | 0.8357   |
| 0.641         | 38.9943 | 1706 | 0.5232          | 0.8579   |
| 0.576         | 40.0    | 1750 | 0.5700          | 0.8393   |
| 0.6097        | 40.9829 | 1793 | 0.5384          | 0.8471   |
| 0.6016        | 41.9886 | 1837 | 0.5824          | 0.8379   |
| 0.6017        | 42.9943 | 1881 | 0.5511          | 0.8443   |
| 0.5937        | 44.0    | 1925 | 0.5095          | 0.8621   |
| 0.5674        | 44.9829 | 1968 | 0.5299          | 0.8536   |
| 0.5575        | 45.9886 | 2012 | 0.5106          | 0.8507   |
| 0.5709        | 46.9943 | 2056 | 0.5445          | 0.8507   |
| 0.5046        | 48.0    | 2100 | 0.4848          | 0.855    |
| 0.5485        | 48.9829 | 2143 | 0.5097          | 0.8564   |
| 0.4865        | 49.9886 | 2187 | 0.5227          | 0.8471   |
| 0.5505        | 50.9943 | 2231 | 0.5127          | 0.8507   |
| 0.4827        | 52.0    | 2275 | 0.5253          | 0.8493   |
| 0.5121        | 52.9829 | 2318 | 0.5095          | 0.8636   |
| 0.4879        | 53.9886 | 2362 | 0.5053          | 0.8621   |
| 0.5008        | 54.9943 | 2406 | 0.5196          | 0.8521   |
| 0.489         | 56.0    | 2450 | 0.4834          | 0.8657   |
| 0.5019        | 56.9829 | 2493 | 0.4714          | 0.8614   |
| 0.4828        | 57.9886 | 2537 | 0.5019          | 0.8571   |
| 0.4373        | 58.9943 | 2581 | 0.4894          | 0.8679   |
| 0.4444        | 60.0    | 2625 | 0.5093          | 0.8657   |
| 0.4178        | 60.9829 | 2668 | 0.5058          | 0.8614   |
| 0.4081        | 61.9886 | 2712 | 0.4996          | 0.8586   |
| 0.4311        | 62.9943 | 2756 | 0.4973          | 0.8557   |
| 0.425         | 64.0    | 2800 | 0.4627          | 0.8743   |
| 0.4147        | 64.9829 | 2843 | 0.4875          | 0.865    |
| 0.4505        | 65.9886 | 2887 | 0.4918          | 0.8636   |
| 0.3621        | 66.9943 | 2931 | 0.4903          | 0.86     |
| 0.4072        | 68.0    | 2975 | 0.4983          | 0.8564   |
| 0.3883        | 68.9829 | 3018 | 0.4635          | 0.8743   |
| 0.4284        | 69.9886 | 3062 | 0.4582          | 0.8686   |
| 0.3891        | 70.9943 | 3106 | 0.4456          | 0.8793   |
| 0.4255        | 72.0    | 3150 | 0.4760          | 0.87     |
| 0.425         | 72.9829 | 3193 | 0.4905          | 0.8721   |
| 0.4301        | 73.9886 | 3237 | 0.4942          | 0.8643   |
| 0.3666        | 74.9943 | 3281 | 0.4824          | 0.8629   |
| 0.4275        | 76.0    | 3325 | 0.4638          | 0.8671   |
| 0.4161        | 76.9829 | 3368 | 0.4859          | 0.8621   |
| 0.3773        | 77.9886 | 3412 | 0.4918          | 0.8521   |
| 0.3591        | 78.9943 | 3456 | 0.4881          | 0.8729   |
| 0.4018        | 80.0    | 3500 | 0.4681          | 0.8707   |
| 0.404         | 80.9829 | 3543 | 0.4882          | 0.86     |
| 0.3987        | 81.9886 | 3587 | 0.4796          | 0.8657   |
| 0.3546        | 82.9943 | 3631 | 0.4945          | 0.8643   |
| 0.3795        | 84.0    | 3675 | 0.4638          | 0.8679   |
| 0.4007        | 84.9829 | 3718 | 0.4624          | 0.8729   |
| 0.3783        | 85.9886 | 3762 | 0.4693          | 0.8729   |
| 0.3498        | 86.9943 | 3806 | 0.4980          | 0.8621   |
| 0.3477        | 88.0    | 3850 | 0.4705          | 0.8671   |
| 0.4022        | 88.9829 | 3893 | 0.4817          | 0.86     |
| 0.3697        | 89.9886 | 3937 | 0.4763          | 0.8629   |
| 0.3828        | 90.9943 | 3981 | 0.4867          | 0.8671   |
| 0.3842        | 92.0    | 4025 | 0.4911          | 0.865    |
| 0.3562        | 92.9829 | 4068 | 0.4562          | 0.875    |
| 0.3343        | 93.9886 | 4112 | 0.4573          | 0.8786   |
| 0.3521        | 94.9943 | 4156 | 0.4481          | 0.8843   |
| 0.3788        | 96.0    | 4200 | 0.4793          | 0.8721   |
| 0.3518        | 96.9829 | 4243 | 0.4802          | 0.8693   |
| 0.3491        | 97.9886 | 4287 | 0.4740          | 0.8686   |
| 0.4063        | 98.2857 | 4300 | 0.5132          | 0.8636   |


### 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