---
library_name: transformers
license: other
base_model: apple/mobilevit-small
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: MobileViT_Food_80epoch
  results: []
---

<!-- 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. -->

# MobileViT_Food_80epoch

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: 0.7769
- Accuracy: 0.8053

## 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: 80

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 4.5902        | 0.9994  | 1183  | 4.5818          | 0.0286   |
| 4.2708        | 1.9996  | 2367  | 4.2247          | 0.1690   |
| 3.7077        | 2.9998  | 3551  | 3.5174          | 0.2602   |
| 3.271         | 4.0     | 4735  | 2.9216          | 0.3432   |
| 2.8193        | 4.9994  | 5918  | 2.4241          | 0.4276   |
| 2.4733        | 5.9996  | 7102  | 2.0284          | 0.5017   |
| 2.1674        | 6.9998  | 8286  | 1.7180          | 0.5674   |
| 1.9884        | 8.0     | 9470  | 1.5144          | 0.6122   |
| 1.7582        | 8.9994  | 10653 | 1.3711          | 0.6450   |
| 1.4781        | 9.9996  | 11837 | 1.2530          | 0.6689   |
| 1.6275        | 10.9998 | 13021 | 1.1598          | 0.6924   |
| 1.5292        | 12.0    | 14205 | 1.1260          | 0.7046   |
| 1.3675        | 12.9994 | 15388 | 1.0912          | 0.7122   |
| 1.3782        | 13.9996 | 16572 | 1.0276          | 0.7255   |
| 1.3084        | 14.9998 | 17756 | 1.0042          | 0.7345   |
| 1.1715        | 16.0    | 18940 | 0.9771          | 0.7427   |
| 1.2386        | 16.9994 | 20123 | 0.9601          | 0.7461   |
| 1.1787        | 17.9996 | 21307 | 0.9489          | 0.7472   |
| 1.1716        | 18.9998 | 22491 | 0.9360          | 0.7516   |
| 1.1363        | 20.0    | 23675 | 0.9129          | 0.7595   |
| 1.2677        | 20.9994 | 24858 | 0.9007          | 0.7633   |
| 1.2019        | 21.9996 | 26042 | 0.8869          | 0.7657   |
| 1.0633        | 22.9998 | 27226 | 0.8835          | 0.7656   |
| 1.0393        | 24.0    | 28410 | 0.8742          | 0.7693   |
| 0.9558        | 24.9994 | 29593 | 0.8704          | 0.7705   |
| 1.0596        | 25.9996 | 30777 | 0.8455          | 0.7764   |
| 1.0749        | 26.9998 | 31961 | 0.8431          | 0.7793   |
| 0.9913        | 28.0    | 33145 | 0.8332          | 0.7795   |
| 0.9477        | 28.9994 | 34328 | 0.8434          | 0.7777   |
| 0.9681        | 29.9996 | 35512 | 0.8215          | 0.7840   |
| 0.9356        | 30.9998 | 36696 | 0.8050          | 0.7888   |
| 0.806         | 32.0    | 37880 | 0.8152          | 0.7870   |
| 1.0011        | 32.9994 | 39063 | 0.8089          | 0.7843   |
| 0.9268        | 33.9996 | 40247 | 0.8018          | 0.7884   |
| 0.8209        | 34.9998 | 41431 | 0.8147          | 0.7876   |
| 0.8193        | 36.0    | 42615 | 0.8043          | 0.7893   |
| 0.8523        | 36.9994 | 43798 | 0.8014          | 0.7893   |
| 0.9134        | 37.9996 | 44982 | 0.7995          | 0.7895   |
| 0.9263        | 38.9998 | 46166 | 0.7928          | 0.7896   |
| 0.9393        | 40.0    | 47350 | 0.7951          | 0.7952   |
| 0.8028        | 40.9994 | 48533 | 0.7840          | 0.7967   |
| 0.8299        | 41.9996 | 49717 | 0.7994          | 0.7929   |
| 0.791         | 42.9998 | 50901 | 0.7873          | 0.7921   |
| 0.8739        | 44.0    | 52085 | 0.7869          | 0.7956   |
| 0.8777        | 44.9994 | 53268 | 0.7835          | 0.7952   |
| 0.8077        | 45.9996 | 54452 | 0.7815          | 0.7957   |
| 0.9119        | 46.9998 | 55636 | 0.7753          | 0.7984   |
| 0.9867        | 48.0    | 56820 | 0.7824          | 0.7969   |
| 0.8115        | 48.9994 | 58003 | 0.7852          | 0.7975   |
| 0.779         | 49.9996 | 59187 | 0.7815          | 0.7992   |
| 0.755         | 50.9998 | 60371 | 0.7796          | 0.8011   |
| 0.7529        | 52.0    | 61555 | 0.7739          | 0.8014   |
| 0.6878        | 52.9994 | 62738 | 0.7914          | 0.7989   |
| 0.744         | 53.9996 | 63922 | 0.7774          | 0.8002   |
| 0.7346        | 54.9998 | 65106 | 0.7679          | 0.8012   |
| 0.7672        | 56.0    | 66290 | 0.7696          | 0.7998   |
| 0.8018        | 56.9994 | 67473 | 0.7877          | 0.7987   |
| 0.7507        | 57.9996 | 68657 | 0.7903          | 0.7979   |
| 0.7632        | 58.9998 | 69841 | 0.7831          | 0.8010   |
| 0.7013        | 60.0    | 71025 | 0.7799          | 0.7985   |
| 0.7364        | 60.9994 | 72208 | 0.7527          | 0.8079   |
| 0.8036        | 61.9996 | 73392 | 0.7664          | 0.8010   |
| 0.74          | 62.9998 | 74576 | 0.7683          | 0.8022   |
| 0.6531        | 64.0    | 75760 | 0.7548          | 0.8021   |
| 0.7375        | 64.9994 | 76943 | 0.7623          | 0.8022   |
| 0.7228        | 65.9996 | 78127 | 0.7820          | 0.8028   |
| 0.7318        | 66.9998 | 79311 | 0.7625          | 0.8008   |
| 0.6529        | 68.0    | 80495 | 0.7693          | 0.8036   |
| 0.68          | 68.9994 | 81678 | 0.7371          | 0.8093   |
| 0.7396        | 69.9996 | 82862 | 0.7699          | 0.8040   |
| 0.7388        | 70.9998 | 84046 | 0.7596          | 0.8038   |
| 0.7135        | 72.0    | 85230 | 0.7607          | 0.8043   |
| 0.6667        | 72.9994 | 86413 | 0.7666          | 0.8034   |
| 0.6866        | 73.9996 | 87597 | 0.7640          | 0.8046   |
| 0.6601        | 74.9998 | 88781 | 0.7573          | 0.8037   |
| 0.7305        | 76.0    | 89965 | 0.7443          | 0.8094   |
| 0.7507        | 76.9994 | 91148 | 0.7636          | 0.8053   |
| 0.7073        | 77.9996 | 92332 | 0.7692          | 0.8033   |
| 0.688         | 78.9998 | 93516 | 0.7609          | 0.8044   |
| 0.6694        | 79.9493 | 94640 | 0.7769          | 0.8053   |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1