ILSVRC 2014 数据集为 Large Scale Visual Recognition Challenge 2014 年比赛的训练数据。Large Scale Visual Recognition Challenge 是针对图像和视频进行物体识别、物体分类、场景识别等任务的算法竞赛。

Dataset 1: Detection

As in ILSVRC2013 there will be object detection task similar in style to PASCAL VOC Challenge. There are 200 basic-level categories for this task which are fully annotated on the test data, i.e. bounding boxes for all categories in the image have been labeled. The categories were carefully chosen considering different factors such as object scale, level of image clutterness, average number of object instance, and several others. Some of the test images will contain none of the 200 categories.

NEW: The training set of the detection dataset will be significantly expanded this year compared to ILSVRC2013. 60658 new images have been collected from Flickr using scene-level queries. These images were fully annotated with the 200 object categories, yielding 132953 new bounding box annotations.

Comparative scale

                                                                       Comparative statistics (on validation set)

Example ILSVRC2014 images:

Dataset 2: Classification and localization

The data for the classification and localization tasks will remain unchanged from ILSVRC 2012 and ILSVRC 2013 . The validation and test data will consist of 150,000 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. The remaining images will be used for evaluation and will be released without labels at test time.

The training data, the subset of ImageNet containing the 1000 categories and 1.2 million images, will be packaged for easy downloading. The validation and test data for this competition are not contained in the ImageNet training data.