pictograph/foodseg103

View on Pictograph · Pictograph Research · Custom (FoodSeg103 dual license)

About

FoodSeg103 is a computer-vision dataset curated and annotated on Pictograph. The most common detected objects are plate, bread, salad, sandwich, cake, broccoli. On Pictograph you can browse every annotated image, fork it into your own workspace in one click, export it in a dozen formats, or train a model on it directly.

At a glance

Metric	Value
Images	7,118
Annotations	40,275
Classes	103
Annotation types	polygon
Splits	train

Quick start

Load it in one line with the datasets library, then read each record's boxes and class names:

from datasets import load_dataset
from PIL import ImageDraw

ds = load_dataset("pictograph/foodseg103", split="train")
example = ds[0]
image = example["image"]            # a PIL image
objects = example["objects"]        # {bbox, categories, category_names}

# draw every bounding box with its class name
draw = ImageDraw.Draw(image)
for (x, y, w, h), name in zip(objects["bbox"], objects["category_names"]):
    draw.rectangle([x, y, x + w, y + h], outline="red", width=3)
    draw.text((x, y - 12), name, fill="red")

# polygon masks live under example["segmentation"]:
#   [{"label": name, "category": idx, "points": [[x, y], ...]}, ...]
image.show()

Prefer a full annotation editor, one-click fork, multi-format export, and one-click training? Open this dataset on Pictograph.

Dataset structure

This dataset uses the Hugging Face imagefolder layout: each split directory holds the images plus a metadata.jsonl that links every image to its annotations by file_name.

Field	Description
file_name	Path to the image within the split directory.
objects.bbox	Bounding boxes as [x, y, width, height] (pixels).
objects.categories	Integer class index per box (matches the class list below).
objects.category_names	Human class name per box.
segmentation	List of {label, category, points}; points is a polygon ring [[x, y], ...].

Data instance

One record (bounding boxes are [x, y, width, height] in pixels; the class index maps into the class list below):

{
  "image": <PIL.Image (RGB)>,
  "objects": {
    "bbox": [[172.0, 192.0, 249.4, 152.7]],
    "categories": [0],
    "category_names": ["plate"]
  },
  "segmentation": [
    {"label": "plate", "category": 0, "points": [[176.9, 207.2], [259.0, 274.1], ...]}
  ]
}

Classes

Class index matches objects.categories in metadata.jsonl.

All 103 classes, in index order: candy, egg_tart, french_fries, chocolate, biscuit, popcorn, pudding, ice_cream, cheese_butter, cake, wine, milkshake, coffee, juice, milk, tea, almond, red_beans, cashew, dried_cranberries, soy, walnut, peanut, egg, apple, date, apricot, avocado, banana, strawberry, cherry, blueberry, raspberry, mango, olives, peach, lemon, pear, fig, pineapple, grape, kiwi, melon, orange, watermelon, steak, pork, chicken_duck, sausage, fried_meat, lamb, sauce, crab, fish, shellfish, shrimp, soup, bread, corn, hamburg, pizza, hanamaki_baozi, wonton_dumplings, pasta, noodles, rice, pie, tofu, eggplant, potato, garlic, cauliflower, tomato, kelp, seaweed, spring_onion, rape, ginger, okra, lettuce, pumpkin, cucumber, white_radish, carrot, asparagus, bamboo_shoots, broccoli, celery_stick, cilantro_mint, snow_peas, cabbage, bean_sprouts, onion, pepper, green_beans, french_beans, king_oyster_mushroom, shiitake, enoki_mushroom, oyster_mushroom, white_button_mushroom, salad, other_ingredients.

License

Annotations and benchmark code: Apache License 2.0 (the FoodSeg103 authors). Images: curated from the Recipe1M dataset (MIT CSAIL, im2recipe) and subject to Recipe1M terms of use - non-commercial research/educational use, registration-gated, redistribution not granted. Attribute both FoodSeg103 (Wu et al., ACM MM 2021) and Recipe1M (Marin et al.).

Source and attribution

This dataset is derived from FoodSeg103, created by Wu et al. (ACM MM 2021). We are grateful to the original authors. If you use this data, please cite the original source above.

Citation

@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 C.H. and Sun, Qianru},
  booktitle={ACM MM},
  year={2021}
}

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