腾讯AI-lab多标签图像数据集，最大的开源多标签图像数据库

1、ML-Images数据集的全部图像URLs,以及相应的类别标注。出于原始图像版权的考虑，此次开源将不直接提供原始图像，用户可利用我们提供的下载代码和URLs自行下载图像。 2、ML-Images数据集的详细介绍，包括图像来源，图像数量，类别数量，类别的语义标签体系，标注方法，以及图像的标注数量等统计量。 3、完整的代码和模型。我们提供的代码涵盖从图像下载，图像预处理，基于ML-Images的预训练，基于ImageNet的迁移学习，到基于训练所得模型的图像特征提取的完整流程。该项目提供了基于小数据集的训练示例，以方便用户快速体验我们的训练流程。该项目还提供了非常高精度的ResNet-101模型(在单标签基准数据集ImageNet的验证集上的top-1精度为80.73%)。用户可根据自身需求，随意选用该项目的代码或模型。 该项目的开源，是腾讯AI Lab在计算机视觉领域所累积的基础能力的一次释放，为人工智能领域的科研人员和工程师提供了充足的高质量训练数据，及简单易用、性能强大的深度学习模型，为包括图像、视频等在内的视觉任务提供强大支撑，并助力图像分类、物体检测、物体跟踪、语义分割等技术水平的提升，促进人工智能行业共同发展。

1. Full image URLs and corresponding category annotations for the ML-Images dataset. Due to copyright concerns of the original images, the raw image files will not be directly provided in this open-source release. Users can download the images on their own using the provided download code and URLs.
2. Detailed introduction of the ML-Images dataset, including image sources, total number of images, number of categories, semantic label system for categories, annotation methods, and statistical metrics such as the number of annotated images.
3. Complete code and models. The provided code covers the entire workflow from image downloading, image preprocessing, pre-training based on ML-Images, transfer learning based on ImageNet, to image feature extraction using the trained models. This project provides training examples on small datasets to enable users to quickly familiarize themselves with our training pipeline. It also offers a highly accurate ResNet-101 model, which achieves a top-1 accuracy of 80.73% on the validation set of the single-label benchmark dataset ImageNet. Users can freely choose to use the code or models from this project according to their own needs.
This open-sourcing of the project represents a release of the foundational capabilities accumulated by Tencent AI Lab in the field of computer vision. It provides abundant high-quality training data and easy-to-use, high-performance deep learning models for researchers and engineers in the artificial intelligence domain, offering strong support for visual tasks including those related to images and videos, and helping to advance technologies such as image classification, object detection, object tracking, and semantic segmentation, so as to promote the common development of the artificial intelligence industry.

搜集汇总

背景与挑战

背景概述

腾讯AI-lab多标签图像数据集是一个开源的多标签图像数据库，提供图像URLs和类别标注（不包含原始图像），并配套完整的处理代码和预训练模型。该数据集旨在支持计算机视觉领域的各类任务，如图像分类、物体检测等。