![]() Download the whole database of ImageNet.One can obtain the training/validation images of our database through the following steps: Thus, here we provide the original image IDs of ImageNet used in our database. We find that massive urls provided by ImageNet have expired (please check the file List of all image URLs of Fall 2011 Release at ). val_urls_from_openimages.txt ( Link1, Link2).train_urls_from_openimages.txt ( Link1, Link2).val_image_id_from_imagenet.txt ( Link1, Link2).train_image_id_from_imagenet.txt ( Link1, Link2).However, one can obtain all images of our database using the following files: Download Imagesĭue to the copyright, we cannot provide the original images directly. Part 2: From Open Images, we adopt 6,902,811 training and 38,739 validation image URLs, covering 1,134 unique categories (note that some other categories are merged with their synonymous categories from ImageNet).įinally, ML-Images includes 17,609,752 training and 88,739 validation image URLs, covering 11,166 categories.Part 1: From the whole database of ImageNet, we adopt 10,706,941 training and 50,000 validation image URLs, covering 10,032 categories.The image URLs of ML-Images are collected from ImageNet and Open Images. We simplify the procedure of downloading images.It presents more details of the database, the loss function, the training algorithm, and more experimental results. Our manuscript of this open-source project has been accepted to IEEE Access ( Journal, ArXiv).Resnet-101 model: it is pre-trained on ML-Images, and achieves the top-1 accuracy 80.73% on ImageNet via transfer learning.ML-Images: the largest open-source multi-label image database, including 17,609,752 training and 88,739 validation image URLs, which are annotated with up to 11,166 categories.This repository introduces the open-source project dubbed Tencent ML-Images, which publishes
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