Datasets:

EduardoPacheco
/

FoodSeg103

   download_size: 1259085777
   dataset_size: 1239854877.226
 ---
+# Dataset Card for FoodSeg103
+## Table of Contents
+- [Dataset Card for FoodSeg103](#dataset-card-for-foodseg103)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Dataset Structure](#dataset-structure)
+    - [Data categories](#data-categories)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Source Data](#source-data)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Refinement process](#refinement-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+  - [Additional Information](#additional-information)
+    - [Dataset Curators](#dataset-curators)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+## Dataset Description
+- **Homepage:** [Dataset homepage](https://xiongweiwu.github.io/foodseg103.html)
+- **Repository:** [FoodSeg103-Benchmark-v1](https://github.com/LARC-CMU-SMU/FoodSeg103-Benchmark-v1)
+- **Paper:** [A Large-Scale Benchmark for Food Image Segmentation](https://arxiv.org/pdf/2105.05409.pdf)
+- **Point of Contact:** [Bert De Brabandere](mailto:[email protected])
+### Dataset Summary
+FoodSeg103 is a large-scale benchmark for food image segmentation. It contains 103 food categories and 7118 images with ingredient level pixel-wise annotations. The dataset is a curated sample from [Recipe1M](https://github.com/facebookresearch/inversecooking) and annotated and refined by human annotators. The dataset is split into 2 subsets: training set, validation set. The training set contains 4983 images and the validation set contains 2135 images.
+### Supported Tasks and Leaderboards
+No leaderboard is available for this dataset at the moment.
+## Dataset Structure
+### Data categories
+| id | ingridient |
+| --- | ---- |
+| 0 | background |
+| 1 | candy |
+| 2 | egg tart |
+| 3 | french fries |
+| 4 | chocolate |
+| 5 | biscuit |
+| 6 | popcorn |
+| 7 | pudding |
+| 8 | ice cream |
+| 9 | cheese butter |
+| 10 | cake |
+| 11 | wine |
+| 12 | milkshake |
+| 13 | coffee |
+| 14 | juice |
+| 15 | milk |
+| 16 | tea |
+| 17 | almond |
+| 18 | red beans |
+| 19 | cashew |
+| 20 | dried cranberries |
+| 21 | soy |
+| 22 | walnut |
+| 23 | peanut |
+| 24 | egg |
+| 25 | apple |
+| 26 | date |
+| 27 | apricot |
+| 28 | avocado |
+| 29 | banana |
+| 30 | strawberry |
+| 31 | cherry |
+| 32 | blueberry |
+| 33 | raspberry |
+| 34 | mango |
+| 35 | olives |
+| 36 | peach |
+| 37 | lemon |
+| 38 | pear |
+| 39 | fig |
+| 40 | pineapple |
+| 41 | grape |
+| 42 | kiwi |
+| 43 | melon |
+| 44 | orange |
+| 45 | watermelon |
+| 46 | steak |
+| 47 | pork |
+| 48 | chicken duck |
+| 49 | sausage |
+| 50 | fried meat |
+| 51 | lamb |
+| 52 | sauce |
+| 53 | crab |
+| 54 | fish |
+| 55 | shellfish |
+| 56 | shrimp |
+| 57 | soup |
+| 58 | bread |
+| 59 | corn |
+| 60 | hamburg |
+| 61 | pizza |
+| 62 |  hanamaki baozi |
+| 63 | wonton dumplings |
+| 64 | pasta |
+| 65 | noodles |
+| 66 | rice |
+| 67 | pie |
+| 68 | tofu |
+| 69 | eggplant |
+| 70 | potato |
+| 71 | garlic |
+| 72 | cauliflower |
+| 73 | tomato |
+| 74 | kelp |
+| 75 | seaweed |
+| 76 | spring onion |
+| 77 | rape |
+| 78 | ginger |
+| 79 | okra |
+| 80 | lettuce |
+| 81 | pumpkin |
+| 82 | cucumber |
+| 83 | white radish |
+| 84 | carrot |
+| 85 | asparagus |
+| 86 | bamboo shoots |
+| 87 | broccoli |
+| 88 | celery stick |
+| 89 | cilantro mint |
+| 90 | snow peas |
+| 91 |  cabbage |
+| 92 | bean sprouts |
+| 93 | onion |
+| 94 | pepper |
+| 95 | green beans |
+| 96 | French beans |
+| 97 | king oyster mushroom |
+| 98 | shiitake |
+| 99 | enoki mushroom |
+| 100 | oyster mushroom |
+| 101 | white button mushroom |
+| 102 | salad |
+| 103 | other ingredients |
+### Data Splits
+This dataset only contains two splits. A training split and a validation split with 4983 and 2135 images respectively.
+## Dataset Creation
+### Curation Rationale
+Select images from a large-scale recipe dataset and annotate them with pixel-wise segmentation masks.
+### Source Data
+The dataset is a curated sample from [Recipe1M](https://github.com/facebookresearch/inversecooking).
+#### Initial Data Collection and Normalization
+After selecting the source of the data two more steps were added before image selection.
+1. Recipe1M contains 1.5k ingredient categoris, but only the top 124 categories were selected + a 'other' category (further became 103).
+2. Images should contain between 2 and 16 ingredients.
+3. Ingredients should be visible and easy to annotate.
+Which then resulted in 7118 images.
+### Annotations
+#### Annotation process
+Third party annotators were hired to annotate the images respecting the following guidelines:
+1. Tag ingredients with appropriate categories.
+2. Draw pixel-wise masks for each ingredient.
+3. Ignore tiny regions (even if contains ingredients) with area covering less than 5% of the image.
+#### Refinement process
+The refinement process implemented the following steps:
+1. Correct mislabelled ingredients.
+2. Deleting unpopular categories that are assigned to less than 5 images (resulting in 103 categories in the final dataset).
+3. Merging visually similar ingredient categories (e.g. orange and citrus)
+#### Who are the annotators?
+A third party company that was not mentioned in the paper.
+## Additional Information
+### Dataset Curators
+Authors of the paper [A Large-Scale Benchmark for Food Image Segmentation](https://arxiv.org/pdf/2105.05409.pdf).
+### Licensing Information
+[Apache 2.0 license.](https://github.com/LARC-CMU-SMU/FoodSeg103-Benchmark-v1/blob/main/LICENSE)
+### Citation Information
+```bibtex
+@inproceedings{wu2021foodseg,
+	title={A Large-Scale Benchmark for Food Image Segmentation},
+	author={Wu, Xiongwei and Fu, Xin and Liu, Ying and Lim, Ee-Peng and Hoi, Steven CH and Sun, Qianru},
+	booktitle={Proceedings of ACM international conference on Multimedia},
+	year={2021}
+}
+```