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Food-101

Introduction

The purpose of this project is to classify images of food dishes into 101 classes. This is a huge dataset with 101,000 images. There are 1,000 images per class which is split into 750 training images and 250 test images.

88.2% Top-1 accuracy, 97.9% Top-5 accuracy was achieved using and optimizing multiple data augmentation techniques, mixup, and inference with TTA.

Overview

The gentlemen who published this dataset stated that training images have some noise in the form of wrong labels and intense colors.

We introduce a challenging data set of 101 food categories, with 101'000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels.

If we look at some images from the dataset, we can see that these images are not taken by professionals with many images are over-exposed or under-exposed. Some of the images contain multiple dishes as well.

Examples of under-exposed image

Examples of over-exposed image

Example of image with multiple dishes

Example of a wrong label for chicken_wings!

The dataset also has intra-class variance which makes it more difficult. Furthermore, there are similarity between some classes which makes it a hard task even for humans.

Sample of images for falafel

All the above suggests that this is a really challenging one.

Methodology

The following summarizes the steps taken to tackle this project:

  1. Initial experimentation to read and split the data, perform data augmentation, and choosing the architecture. Details are available in this notebook.
  2. Build, train, and test and model. Details are available in this notebook.
  3. Interpretation of the results. Details are available in here.

Challenges

  • Initially, it was difficult to read, label, and split the dataset as it doesn't conform with other popular datasets like ImageNet or MNIST. I've seen many posts online that did lots of preprocessing to bring the dataset to a more popular format, but it didn't make much sense to me. Especially, given the large size of the dataset. This gave me the chance to dive into the details of fastai's Datablock API. I struggled with labeling using label_from_folder function, which assumed that files are read from a folder while I read it through a dataframe. I raised this issue and was addressed quickly by the fastai team.

  • Choosing the right augmentation parameters was a bit of a hassle, but visually inspecting the results helped a lot.

  • Fine tuning certain parameters like dropout rate and weight decay took some time and had a great effect on the performance of the model.