Computer Vision Images Datasets

Description

In order to facilitate the use of public datasets in 'Counting on me' , several commonly used foreign datasets about Computer Vison Images are mirrored and backed up here:

1. COCO

Introduction: COCO is a large-scale object detection, segmentation, and captioning dataset. COCO has several features: Object segmentation, Recognition in context, Superpixel stuff segmentation, 330K images (>200K labeled), 1.5 million object instances, 80 object categories, 91 stuff categories, 5 captions per image, 250,000 people with keypoints.

Website: https://cocodataset.org

Command: 

ssh username@data.hpc.sjtu.edu.cn

cp /lustre/share/scidata/coco.tar.gz ~/target_position/

Citation:

Lin T Y, Maire M, Belongie S, et al. Microsoft coco: Common objects in context[C]//Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13. Springer International Publishing, 2014: 740-755.

2. Tiny ImageNet

Introduction: The training set of a miniature of ImageNet classification Challenge. MicroImageNet classification challenge is similar to the classification challenge in the full ImageNet ILSVRC. MicroImageNet contains 200 classes for training. Each class has 500 images. The test set contains 10,000 images. All images are 64x64 colored ones.

Website: https://www.kaggle.com/c/tiny-imagenet/overview

Command:

ssh username@data.hpc.sjtu.edu.cn

cp /lustre/share/scidata/tiny-imagenet-200.zip ~/target_position/

Citation:

mnmoustafa, Mohammed Ali. (2017). Tiny ImageNet. Kaggle. https://kaggle.com/competitions/tiny-imagenet

3. ILSVRC2010

Introduction: The training datais the subset of ImageNet containing the 1000 categories and 1.2 million images. The validation and test data for this competition will consist of 200,000 photographs, collected from flickr and other search engines, hand labeled with the presence or absence of 1000 object categories. The 1000 object categories contain both internal nodes and leaf nodes of ImageNet, but do not overlap with each other. A random subset of 50,000 of the images with labels will be released as validation data included in the development kit along with a list of the 1000 categories.

Website: https://image-net.org/challenges/LSVRC/2010/index.php

Command:

ssh username@data.hpc.sjtu.edu.cn

cp /lustre/share/scidata/ILSVRC2010_images_train.tar ~/target_position/
cp /lustre/share/scidata/ILSVRC2010_images_val.tar ~/target_position/

Citation:

Russakovsky, O., Deng, J., Su, H. et al. ImageNet Large Scale Visual Recognition Challenge. Int J Comput Vis 115, 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y

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