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Tencent AI Lab officially open source industry's largest multi-label image dataset

via:博客园     time:2018/10/18 20:32:31     readed:88

Today, Tencent AI Lab announced the official open source “Tencent ML-Images” project, which consists of the multi-label image dataset ML-Images and the most accurate deep residual network ResNet-101 in the industry's current deep learning model.

The open source of the project is a release of the basic capabilities accumulated by Tencent AI Lab in the field of computer vision, providing sufficient high-quality training data for researchers and engineers in the field of artificial intelligence, and easy-to-use, powerful deep learning. The model promotes the common development of the artificial intelligence industry.

Project access address:

https://github.com/Tencent/tencent-ml-images

The image dataset ML-Images released by Tencent AI Lab contains 18 million images and more than 11,000 common object categories. The largest multi-label image dataset in the industry is large enough to meet the needs of general scientific research institutions and small and medium-sized enterprises. scenes to be used. In addition, the deep residual network ResNet-101 based on ML-Images training has excellent visual representation and generalization performance. It has the highest precision in the current model in the industry and will provide visual tasks including images and videos. Strong support, and help improve the technical level of image classification, object detection, object tracking, semantic segmentation.

This official open source, its main contents include:

  1. All image URLs for the ML-Images dataset, along with the corresponding category annotations. Due to the original image copyright issue, this open source will not directly provide the original image, users can download images by using the download code and URLs provided by Tencent AI Lab.
  2. A detailed introduction to the ML-Images data set. Includes statistics such as image source, number of images, number of categories, semantic labeling system for categories, labeling methods, and number of labels for images.
  3. Complete code and model. The code provided by Tencent AI Lab covers the complete process from image download and image preprocessing, to ML-Images based pre-training and ImageNet-based migration learning, to image feature extraction based on trained models. The project provides training examples based on small data sets to facilitate the user to quickly experience the training process. The project also offers the ResNet-101 model with extremely high accuracy (top-1 accuracy of 80.73% on the verification set of the single-label benchmark dataset ImageNet). Users can choose the code or model of the project according to their own needs.

The deep learning technology represented by deep neural network has fully demonstrated its excellent capabilities in many fields, especially in the field of computer vision, including the important tasks of classification, understanding and generation of images and videos. However, in order to give full play to the visual representation of deep learning, it must be based on sufficient high-quality training data, excellent model structure and model training methods, and strong computing resources and other basic capabilities.

Major technology companies have placed great emphasis on the building of the basic capabilities of artificial intelligence, and have built large image datasets that are only for their internals, such as Google's JFT-300M and Facebook's Instagram dataset. However, these data sets and the models they have trained are not disclosed. For general scientific research institutions and small and medium-sized enterprises, these artificial intelligence basic capabilities have very high thresholds.

The largest multi-label image dataset currently available in the industry is Google's Open Images, which includes 9 million images and more than 6000 object categories. Tencent AI Lab's open source ML-Images dataset includes 18 million images and more than 11,000 common object categories, or will become the new industry benchmark dataset.

In addition, the ResNet-101 model based on ML-Images training has excellent visual representation and generalization performance. Through migration learning, the model achieved 80.73% top-1 classification accuracy on the ImageNet validation set, exceeding the accuracy of Google's homogeneous model (migration learning mode), and it is worth noting that ML-Images is only JFT-300M. About 1/17. This fully demonstrates the effectiveness of ML-Images' quality and training methods. Compare the table below in detail.

orgsrc=//img2018.cnblogs.com/news/66372/201810/66372-20181018172421530-349605933.jpg

Note: The Microsoft ResNet-101 model is trained for non-migration learning mode, ie the 1.2M pre-trained image is the image of the original dataset ImageNet.

Tencent AI Lab's open-source "Tencent ML-Images" project demonstrates Tencent's efforts in building the basic capabilities of artificial intelligence and the vision of promoting the common development of the industry through the opening of basic capabilities.

“Tencent ML-Images”, the deep learning model of the project, has played an important role in many business activities of Tencent, such as “image quality evaluation and recommendation function” of “Daily Express”, the daily call amount of the background test has reached 1000. Million times.

As shown in the figure below, the quality of the cover image of the Daily Express News has been significantly improved.

orgsrc=//img2018.cnblogs.com/news/66372/201810/66372-20181018172421656-666355217.jpg

The picture on the left is before optimization and the picture on the right is after optimization.

In addition, the Tencent AI Lab team migrated the ResNet-101 model based on Tencent ML-Images to many other visual tasks, including image object detection, image semantic segmentation, video object segmentation, and video object tracking. These visual migration tasks further validate the model's powerful visual representation and excellent generalization performance. “Tencent ML-Images” will continue to play an important role in more visually relevant products in the future.

Tencent first released an open source project on GitHub in 2016 (https://github.com/Tencent), there are currently 57 projects open source covering artificial intelligence, mobile development, and small programs. To further contribute to the open source community, Tencent has joined Hyperledger, LF Networking and the Open Network Foundation, and has become a founding member of the LF Deep Learning Foundation and a Platinum Member of the Linux Foundation. As Tencent's “Open” strategy is reflected in the technical field, Tencent Open Source will continue to promote technology research and development to share, reuse and open source, release Tencent's research and development capabilities, provide technical support for domestic and foreign open source communities, and inject research and development. vitality.

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