license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: iraqi_foods102
  results: []
datasets:
- Falah/food102-iraqi-rice-meal
language:
- en
author: Falah G. Salieh
location: Iraq, Baghdad
---




<!-- 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. -->

# iraqi_foods102

This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5399
- Accuracy: 0.8548

## Dataset for Food-102 (Food101+Iraqi-rice-male)

Dataset Name: Food-102

Dataset Summary:
Food-102 is an updated version of the Food-101 dataset, now expanded to include 102 food categories. It consists of a total of 102,000 images, with 750 training images and 250 manually reviewed test images provided for each category. The dataset aims to enable food classification tasks and provide a diverse range of food images for research and development purposes. The training images in Food-102 have intentionally not been cleaned, allowing for some level of noise, such as intense colors and occasional mislabeled images. All images in the dataset have been rescaled to have a maximum side length of 512 pixels.

## Additional Information:
- Number of Categories: 102
- Total Images: 101,100
- Training Images per Category: 75,825
- Test Images per Category: 25,275
- Image Noise: The training images may contain some noise, including intense colors and occasional mislabeled images.
- Image Rescaling: All images in the dataset have been resized to have a maximum side length of 512 pixels.

## Note:
The newly added category "Iraqi rice male food" is not specifically mentioned as part of the Food-101 dataset. If you require further details or have any specific questions about the dataset, please let me know.

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1273        | 1.0   | 592  | 0.7230          | 0.8165   |
| 0.7414        | 2.0   | 1185 | 0.5696          | 0.8478   |
| 0.5882        | 3.0   | 1776 | 0.5399          | 0.8548   |

### Framework versions

- Transformers 4.27.1
- Pytorch 2.0.1+cu118
- Datasets 2.9.0
- Tokenizers 0.13.3

## Citation

If you use this model in your research, please cite the following paper: