In this paper, we introduce a large-scale food images dataset namely AIFood, which is constructed to aim ingredient recognition in food image research. AIFood dataset includes 24 categories and totally 372,095 food images around the world. We collect food images from eight existing food image datasets and a food website. The food images are relabeled using 24 categories. We preliminarily label each image using existing food information, e.g. dish name or ingredient information. Next, we manually check food images to find out undiscovered ingredients and relabel them. Every image can be labeled more than one category. In addition, food images may have color cast or uneven contrast problems, which may disturb performance of image recognition system. So, we applied preprocessing method which contains automatic white balancing and contrast limited adaptive histogram equalization methods to improve visual quality of food images. We set constraints which are defined by luminance and chrominance of image to determine if the image is to be preprocessed.