LIP-专注于对人的语义分割的数据集

Look into Person (LIP) is a new large-scale dataset, focus on semantic understanding of person. Following are the detailed descriptions. The dataset contains 50,000 images with elaborated pixel-wise annotations with 19 semantic human part labels and 2D human poses with 16 key points. The annotated 50,000 images are cropped person instances from COCO datasetwith size larger than 50 * 50.The images collected from the real-world scenarios contain human appearing with challenging poses and views, heavily occlusions, various appearances and low-resolutions. We are working on collecting and annotating more images to increase diversity. We have divided images into three sets. 30462 images for training set, 10000 images for validation set and 10000 for testing set. Besides we have another large dataset mentioned in "Human parsing with contextualized convolutional neural network." ICCV'15, which focuses on fashion images. You can download the dataset including 17000 images as extra training data. To stimulate the multiple-human parsing research, we collect the images with multiple person instances to establish the first standard and comprehensive benchmark for instance-level human parsing. Our Crowd Instance-level Human Parsing Dataset (CIHP) contains 28280 training, 5000 validation and 5000 test images, in which there are 38280 multiple-person images in total. VIP(Video instance-level Parsing) dataset, the first video multi-person human parsing benchmark, consists of 404 videos covering various scenarios. For every 25 consecutive frames in each video, one frame is annotated densely with pixel-wise semantic part categories and instance-level identification. There are 21247 densely annotated images in total. We divide these 404 sequences into 304 train sequences, 50 validation sequences and 50 test sequences. MPV (Multi-Pose Virtual try on) dataset, which consists of 35,687/13,524 person/clothes images, with the resolution of 256x192. Each person has different poses. We split them into the train/test set 52,236/10,544 three-tuples, respectively. Custom