---
# System prepended metadata

title: FoodSeg103

---


# FoodSeg103
![](https://s3.w3s.aioz.network/w3ai-platform-v2/uploads/documents/54deb202-59ab-491b-b167-e0d95f9c4eb7/2024/06/25/1719300416-dyRFroD728BM3Fq4xa6BiN.png?AWSAccessKeyId=FT7EO3IGQNMIILHXIDZRVTJHWE&Signature=HfcTZRZtDHUbCV3Um4pzH0ddEIE%3D&Expires=2350020416)

## Summary
* [Introduction](#introduction)
* [Dataset Structure](#dataset-structure)
* [Reference](#reference)
* [License](#license)
* [Citation](#citation)

## Introduction
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.
## Dataset Structure
### Data categories

```
{
   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.

## Reference

We would like to acknowledge the Wu, Xiongwei et al. for creating and maintaining the FoodSeg103 dataset as a valuable resource for the computer vision and machine learning research community. For more information about the FoodSeg103 dataset and its creator, please visit [the FoodSeg103 website](https://xiongweiwu.github.io/foodseg103.html).


## License 

The dataset has been released under a Apache 2.0 license.

## 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 CH and Sun, Qianru},
    booktitle={Proceedings of ACM international conference on Multimedia},
    year={2021}
}
```