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113
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1
18
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{ "bbox": [ [ 7, 53, 200, 189 ], [ 84, 5, 137, 180 ] ], "categories": [ 28, 57 ], "category_names": [ "banana", "bread" ] }
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{ "bbox": [ [ 117, 69, 394, 314 ], [ 8, 16, 465, 307 ] ], "categories": [ 47, 100 ], "category_names": [ "chicken_duck", "white_button_mushroom" ] }
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{ "bbox": [ [ 50, 63, 163, 138 ], [ 134, 130, 190, 197 ] ], "categories": [ 57, 65 ], "category_names": [ "bread", "rice" ] }
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{ "bbox": [ [ 0, 18, 353, 260 ], [ 240, 3, 211, 155 ], [ 285, 118, 226, 160 ] ], "categories": [ 47, 76, 81 ], "category_names": [ "chicken_duck", "rape", "cucumber" ] }
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FoodSeg103

7,118 images and 40,275 annotations across 103 classes, annotated as polygon.

View on Pictograph · Pictograph Research · Custom License

About

FoodSeg103 is a computer-vision dataset curated and annotated on Pictograph. 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

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 Polygon rings as [[x, y], ...] with class label.

Use it

from datasets import load_dataset

ds = load_dataset("pictograph/foodseg103")
print(ds)

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

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, Fu, Liu, Lim, Hoi, Sun - Singapore Management University (ACM MM 2021), originally licensed Apache License 2.0. We are grateful to the original authors. If you use this data, please cite the original source above.


Published from Pictograph - annotate, train, and deploy from one API.

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