The CORE dataset is intended to help learn more detailed models and for exploring cross-category generalization in object recognition. It has already been used for exploring problems such as:
Describing familiar and unfamiliar objects
Localizing parts for pose, viewpoint, and object parsing
11/1/2010 - Included evaluation code and precomputed detections.
10/12/2010 - Updated Dataset
README The README is also included with the annotations.
6/11/2010 - The website is now up!
Everything you need to get started with the dataset is here; learning and inference tools will be added soon.
Be sure to visit our workshop talk at ACVHL2010 and poster at CVPR2010 this week.
 “Attribute-Centric Recognition for Cross-category Generalization” [ pdf] Ali Farhadi, Ian Endres, and Derek Hoiem
 “The Benefits and Challenges of Collecting Richer Object Annotations” [ pdf] Ian Endres, Ali Farhadi, Derek Hoiem, and David Forsyth
ACVHL 2010 (in conjunction with CVPR).
 “Describing Objects by their Attributes” [ pdf][ related website] Ali Farhadi, Ian Endres, Derek Hoiem, and David A. Forsyth
You can browse the dataset online here.
Annotated Dataset CORE_v1_data.tar.gz (522MB) The base dataset, includes the images and annotations.
The LabelMe toolbox provides many helpful functions for manipulating this dataset:
Detection Evaluation CORE_v1_eval.tar.gz (7.2KB) Code used in  to evaluate part and object predictions on the CORE dataset.
Precomputed Bounding Boxes CORE_v1_detections.tar.gz (3.4GB) Top 1000 bounding boxes for each image from category and part models trained with Felzenszwalb et al.
Dataset Toolbox (Coming Soon) CORE_v1_tools.tar.gz A set of Matlab code to supplement the LabelMe toolbox. This includes code to retrieve, aggregate, and display data from the images. See documentation included in the package.
Object/Part Detection Models (Coming Soon) CORE_v1_det_models.tar.gz These are the models we used for localizing parts and objects in 
These require the detection code from Felzenszwalb et al. (use version 3.x):
Training, Inference, and Evaluation (Coming Soon) CORE_v1_full_model.tar.gz This code in conjunction with the Object/Part Models can be used to reproduce the results found in 
This dataset was supported in part by a Google Research Award, the National Science Foundation under IIS (0904209),
an NSF CAREER Award (1053768), an ONR MURI Award (N000141010934), and gifts from Microsoft and Intel.
Please contact iendres2 -at- uiuc.edu with any questions.