SUN09 Image Segmentation Dataset

The SUN09 dataset consists of 12,000 annotated images covering more than 200 object categories. The dataset contains natural, indoor, and outdoor images. Each image contains an average of 7 different annotated objects, and the average area occupied by each object is 5 % of the image size. The frequency of object categories follows a power-law distribution.

The dataset contains two major benchmarks:

1. Used to evaluate the overall target recognition system, including:

• static_sun09_database: 12,000 annotated images
• static_sun_objects: additional images used for training the baseline detector (not used for training the context model)
• out_of_context: 42 out-of-context images

2. Contextual models for evaluating the output of a pre-computed baseline detector, which contains:

• The file name corresponds to [(test/train)/objectCategory/imageName.txt]
• Each line in the text file shows the bounding box position and score of a candidate window: [ x1 y1 x2 y2 score ]
• We use 4,367 training images and 4,317 testing images. Each group has the same number of images per scene category.
• Load sun09_detectorOutputs.mat for the baseline detector outputs and sun09_groundTruth.mat for the ground truth annotations.

This dataset was published by MIT at the 2010 IEEE CVPR.