Duplicate from EduardoPacheco/FoodSeg103

Co-authored-by: Eduardo Pacheco <EduardoPacheco@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+size_categories:
+- n<1K
+task_categories:
+- image-segmentation
+task_ids:
+- semantic-segmentation
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype: image
+  - name: classes_on_image
+    sequence: int64
+  - name: id
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 1140887299.125
+    num_examples: 4983
+  - name: validation
+    num_bytes: 115180784.125
+    num_examples: 2135
+  download_size: 1254703923
+  dataset_size: 1256068083.25
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+---
+
+# 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:** [Not Defined]
+
+### 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}
+}
+```

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id2label.json

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+{"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"}