SEG-FOOD Semantic Food Segmentation Through Deep Learning

Citation Author(s):
Ghalib Ahmed
Loo Chu
Submitted by:
Chu Kiong Loo
Last updated:
Tue, 05/17/2022 - 22:21
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Semantic segmentation is the topic of interest among deep learning researchers in the recent era.  It has many applications in different domains including, food recognition. In the case of food recognition, it removes the non-food background from the food portion. There is no large public food dataset available to train semantic segmentation models. We prepared a dataset named ’SEG-FOOD’[44] containing images of FOOD101, PFID, and Pakistani Food dataset and open-sourced the annotated dataset for future research. We annotated the images using JS Segment annotator. For experimentation, please refer to our paper, and for the starter code, please refer to our Github repository.

* Please note that our main contribution is a manual annotation by JS-Segment so that researchers can explore various semantic segmentation based methods based on deep learning. The images of this dataset contain images from Food101, PFID dataset, and our own collected dataset of Pakistani Food.


*  For detailed experimentation, please refer to our paper which is under review. we will update the link of that later.

* For starter code please refer to our Github repository.

* Note: This dataset contains images from Food101, PFID, and Pakistani Food Dataset. Our main contribution is the manual annotation of the food images for background removal using semantic segmentation and collection of Pakistani food dataset images. Please cite our work besides the original dataset collector if you are using a segmented dataset otherwise, cite the original dataset collector.



Submitted by Gaurav Joshi on Mon, 01/25/2021 - 00:35

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