---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: food-recognition
  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. -->

# food-recognition

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2610
- Accuracy: 0.9324

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5974        | 0.21  | 100  | 0.6096          | 0.8292   |
| 0.5911        | 0.43  | 200  | 0.5204          | 0.8476   |
| 0.7085        | 0.64  | 300  | 0.4329          | 0.8708   |
| 0.5302        | 0.85  | 400  | 0.4843          | 0.8428   |
| 0.2436        | 1.07  | 500  | 0.3767          | 0.886    |
| 0.2355        | 1.28  | 600  | 0.3344          | 0.8956   |
| 0.1497        | 1.49  | 700  | 0.3447          | 0.8932   |
| 0.2213        | 1.71  | 800  | 0.3082          | 0.9072   |
| 0.2197        | 1.92  | 900  | 0.3169          | 0.902    |
| 0.0719        | 2.13  | 1000 | 0.2977          | 0.9136   |
| 0.0526        | 2.35  | 1100 | 0.3455          | 0.9084   |
| 0.0926        | 2.56  | 1200 | 0.3140          | 0.9208   |
| 0.0427        | 2.77  | 1300 | 0.3307          | 0.9128   |
| 0.0716        | 2.99  | 1400 | 0.3007          | 0.9204   |
| 0.0151        | 3.2   | 1500 | 0.2791          | 0.9292   |
| 0.032         | 3.41  | 1600 | 0.2737          | 0.9296   |
| 0.0611        | 3.62  | 1700 | 0.2620          | 0.9336   |
| 0.0175        | 3.84  | 1800 | 0.2610          | 0.9324   |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.1.1+cpu
- Datasets 2.15.0
- Tokenizers 0.15.0