BACKGROUND Evidence demonstrates that seeing alcoholic beverages in electronic media increases alcohol initiation and frequent and excessive drinking, particularly among young people. To efficiently assess this exposure, the aim was to develop the Alcoholic Beverage Identification Deep Learning Algorithm (ABIDLA) to automatically identify beer, wine and champagne/sparkling wine from images. METHODS Using a specifically developed software, three coders annotated 57,186 images downloaded from Google. Supplemented by 10,000 images from ImageNet, images were split randomly into training data (70 %), validation data (10 %) and testing data (20 %). For retest reliability, a fourth coder re-annotated a random subset of 2004 images. Algorithms were trained using two state-of-the-art convolutional neural networks, Resnet (with different depths) and Densenet-121. RESULTS With a correct classification (accuracy) of 73.75 % when using six beverage categories (beer glass, beer bottle, beer can, wine, champagne, and other images), 84.09 % with three (beer, wine/champagne, others) and 85.22 % with two (beer/wine/champagne, others), Densenet-121 slightly outperformed all Resnet models. The highest accuracy was obtained for wine (78.91 %) followed by beer can (77.43 %) and beer cup (73.56 %). Interrater reliability was almost perfect between the coders and the expert (Kappa = .903) and substantial between Densenet-121 and the coders (Kappa = .681). CONCLUSIONS Free from any response or coding burden and with a relatively high accuracy, the ABIDLA offers the possibility to screen all kinds of electronic media for images of alcohol. Providing more comprehensive evidence on exposure to alcoholic beverages is important because exposure instigates alcohol initiation and frequent and excessive drinking.

中文翻译：

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在电子媒体中，我们有多少接触酒精？酒精饮料识别深度学习算法（ABIDLA）的开发。

背景技术证据表明，在电子媒体中看到含酒精的饮料会增加酒精的摄入以及频繁和过量饮酒，特别是在年轻人中。为了有效地评估这种暴露程度，目标是开发酒精饮料识别深度学习算法（ABIDLA），以从图像中自动识别啤酒，葡萄酒和香槟/起泡酒。方法使用一个专门开发的软件，三个编码器注释了从Google下载的57186张图像。通过来自ImageNet的10,000张图像的补充，图像被随机分为训练数据（70％），验证数据（10％）和测试数据（20％）。为了重新测试可靠性，第四个编码器重新标注了2004年图像的随机子集。使用两个最先进的卷积神经网络对算法进行了训练，Resnet（深度不同）和Densenet-121。结果使用六个饮料类别（啤酒杯，啤酒瓶，啤酒罐，葡萄酒，香槟和其他图像）时，正确分类（准确性）为73.75％，使用三个饮料（啤酒，葡萄酒/香槟酒和其他图像）的正确分类（准确性）为84.09％。 Densenet-121搭配两个（啤酒/葡萄酒/香槟，其他）％，略胜于所有Resnet型号。葡萄酒的准确度最高（78.91％），其次是啤酒罐（77.43％）和啤酒杯（73.56％）。Interrater的可靠性在编码人员和专家之间（Kappa = .903）几乎是完美的，而在Densenet-121和编码人员之间（Kappa = .681）非常重要。结论ABIDLA不受任何响应或编码负担的影响，并且具有相对较高的准确性，因此可以筛查各种电子媒体中的酒精图像。