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LandmarksClassifierAsia : A Machine Learning Model to Identify Japanese Tourist Attractions

This is an introduction to「LandmarksClassifierAsia」, a machine learning model that can be used with ailia SDK. You can easily use this model to create AI applications using ailia SDK as well as many other ready-to-use ailia MODELS.

Overview

LandmarksClassifierAsia is a machine learning model for identifying Japanese tourist attractions published by Google in April 2020. The model can identify 17,771 popular landmarks based on a single image.

Architecture

The model input is a 321x321 RGB image normalized to a range of 0 to 1. The landmark is assumed to be cropped and entered in the input image for proper detection. The output is a similarity score for 98,960 categories with the associated names of landmarks in English. There are 17,771 unique categories therefore some labels are redundant in the output and some post-processing is required to merge duplicates.

For example, if the output vector is [0.3, 0.5, 0.1] and the labels are [label_1, label_2, label_1], the output should be {label_1: 0.3, label_2: 0.5} giving only the highest score among the overlapping labels.

The model was trained on Google Landmarks Dataset V2 (GLDv2). This dataset contains 5 million training images, 200 000 labels, and 110 000 test images. The images were collected from Wikimedia Commons and manually annotated over 800 hours.

Source: https://arxiv.org/abs/2004.01804

Because of the large number of categories of the dataset, distance metric learning was used and performance results were given relative to ResNet101+ArcFace. Checking the model in Netron, it appears that the published model is not ResNet101, but a slightly lighter backbone using kernel sizes 3x3 and 1x1.

The mAP@100 (recognition rate using top-100 of detection results) for the model using ResNet101 and ArcFace is 23.30%. The numerical value appears low because of the huge number of labels.

Source: https://arxiv.org/abs/2004.01804

Results

Here are the results of this model on some input images. We can see that very distinctive landmarks such as Tokyo Tower and Kaminarimon below are perfectly detected.

Source: https://pixabay.com/photos/japan-tokyo-tower-landmark-343444/
TopK predictions:
Tokyo Tower: 92.34%
Sapporo TV Tower: 84.53%
Yokohama Marine Tower: 81.77%
Source: https://pixabay.com/photos/tokyo-asakusa-kaminarimon-gate-2443311/
TopK predictions:
Kaminarimon Gate Senso-ji: 92.01%
Hōzōmon Gate: 89.89%
Osu Kannon: 85.13%

However other less recognizable landmarks such as Mount Fuji and Hamarikyu do not come out as good since mountains and gardens are less distinctive and seem a little more difficult to detect.

Source: https://pixabay.com/photos/mountain-volcano-peak-summit-477832/
TopK predictions:
Asagirikogen Rest Area: 89.05%
Mt. Omuro: 81.65%
Mount Fuji: 81.06%
Source: https://pixabay.com/photos/hamarikyu-japan-garden-lake-path-960271/
TopK predictions:
Kannon-in: 85.25%
Keitakuen Garden: 83.54%
Kyoto Imperial Palace: 83.07%

Usage

You can use LandmarksClassifierAsia with ailia SDK using the following command.

$ python3 landmarks_classifier_asia.py --input input.jpg

ax Inc. has developed ailia SDK, which enables cross-platform, GPU-based rapid inference.

ax Inc. provides a wide range of services from consulting and model creation, to the development of AI-based applications and SDKs. Feel free to contact us for any inquiry.

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