---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swin-food102
  results: []
datasets:
- juliensimon/food102
---

# swin-food102

This model is a fine-tuned version of [juliensimon/autotrain-food101-1471154053](https://huggingface.co/juliensimon/autotrain-food101-1471154053) on the [food102](https://huggingface.co/datasets/juliensimon/food102) dataset, namely the [food101](https://huggingface.co/datasets/food101) dataset with an extra class generated with a Stable Diffusion model. 

A detailed walk-through is available on [YouTube](https://youtu.be/sIe0eo3fYQ4).

The achieves the following results on the evaluation set:
- Loss: 0.2510
- Accuracy: 0.9338

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1648        | 1.0   | 597  | 0.3118          | 0.9218   |
| 0.31          | 2.0   | 1194 | 0.2606          | 0.9322   |
| 0.2488        | 3.0   | 1791 | 0.2510          | 0.9338   |


### Framework versions

- Transformers 4.23.1
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.13.1