Dataset Card for Food-101

  Dataset Summary

This dataset consists 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.

  Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/ethz/food101.

Dataset Details

  Dataset Description

This dataset is the combination of the Food101, Indian Food Classification and The-massive-Indian-Food-Dataset datasets. This Dataset aims to be a viable dataset for Image Classification of Foods with an added Indian context. This dataset has 121 classes with each class having 800 images in the train split and 200 images in the test split. Maximum resolution of images is 512*512. The Food121-224 dataset has all images downscaled to a… See the full description on the dataset page: https://huggingface.co/datasets/ItsNotRohit/Food121.

PFID

We're going to work with are from the Food-101 dataset, a collection of 101 different categories of 101,000 (1000 images per category) real-world images of food dishes.

• A train directory which contains all of the images in the training dataset with subdirectories each named after a certain class containing images of that class.
• A test directory with the same structure as the train directory.

Example of file structure

pizza_steak <- top level folder
└───train <- training images
│  └───pizza
│  │  │  1008104.jpg
│  │  │  1638227.jpg
│  │  │  ...   
│  └───steak
│    │  1000205.jpg
│    │  1647351.jpg
│    │  ...
│  
└───test <- testing images
│  └───pizza
│  │  │  1001116.jpg
│  │  │  1507019.jpg
│  │  │  ...   
│  └───steak
│    │  100274.jpg
│    │  1653815.jpg
│    │  ...  
 ```

Let's inspect each of the directories we've downloaded.

To so do, we can use the command `ls` which stands for list.

Motivation

Waste. As we all know that waste has become commonplace in many countries in the world. There is nothing wrong, in terms of waste itself defined as the final product that can no longer be used (by humans); residue. The problem lies in 'how do we manage this waste, while we can't use it anymore?'. Several countries have issues related to waste because the rate of waste production is not comparable to its management efforts. These things can be a big problem for the ecosystem.

With this dataset. I hope we can help waste management efforts with computer vision technology. With this technology, we can identify, track, sort and process it accordingly.

Content

This dataset contains approximately 256K images (156K original data) representing two classes, Biodegradable and Non-biodegradable. - Biodegradable, contains materials which can be decomposed naturally by microorganisms, such as foods, plants, fruits, etc. The waste from this material can be processed into compost. - Non-biodegradable, contains materials that cannot be decomposed naturally, for example plastics, metals, inorganic elements, etc. The waste from this material will be recycled into new materials.

I add augmented data against imbalanced class. Augmented data made by manipulating original data. Image transformation used: horizontal flip, vertical flip, 90deg CW rotation, 90deg CCW rotation.

In this dataset, I divide the data into two subsets, training set and evaluation set. The training set itself was splitted into 4 parts due to some technical constraints (my internet bandwidth). The thing to know is that the part of the training set don't have a good data distribution. So, don't pass each part directly to your model. Concatenate each part to single dataset. See Quickstart.

Data files in this dataset have unique name to prevent them from overwritten theirself when concatenating. Below is filename reference. You will need this for filtering this dataset.

SUBSET.PART_CLASS_CATEGORY_ID.EXT

SUBSET, the subset where data belong in. Either TEST or TRAIN. PART, part number of subset. Only if the subset splitted into several parts. CLASS, the class of data. BIODEG for biodegradable, or NBIODEG for non-biodegradable. CATEGORY, category of data. ORI for original data, HFL for horizontal flip, VFL for vertical flip, CWR for clockwise rotation, CCW for counter clockwise rotation. ID, data identification number. EXT, data extension. Either .jpg or .jpeg.

Acknowledgements

In this part, i would like to give an attribution to several Kaggle's users because without their great work, this dataset would be incomplete. As i mentioned that this dataset's source consist of another Kaggle dataset. So, this is my responsibility to do this. - Food Images (Food-101) - (K Scott Mader) - Fruit and Vegetable Image Recognition - (Kritik Seth) - Waste Classification data - (Sashaank Sekar) - Waste Classification Data v2 - (sapal6) - waste_pictures - (且听风吟)