Berkeley Segmentation Data Set and Benchmarks 500 (BSDS500)
This new dataset is an extension of the BSDS300, where the original 300 images are used for training / validation and 200 fresh images, together with human annotations, are added for testing. Each image was segmented by five different subjects on average. Performance is evaluated by measuring Precision / Recall on detected boundaries and three additional region-based metrics.
|Original Image||Subject 1||Subject 2||Subject 3|
Best Practice Guidelines: The dataset consists of 500 natural images, ground-truth human annotations and benchmarking code. The data is explicitly separated into disjoint train, validation and test subsets. In order to preserve the integrity of the evaluation and obtain a direct and fair comparison of your results with existing methods, the guidelines below must be followed:
- Train only on trainval: All the learning, parameter tuning, model selection, etc. should be done exclusively on the train and validation subsets of the data.
- Run once on test: After training, your algorithm should be run only once with fixed parameters on the test subset of the data. The images and ground-truth segmentations of the test set cannot be used for tuning your algorithm.
- Report all the evaluation results: Evaluate your results on the test subset with the benchmarking code. In order to assess quantitatively different aspects of performance of contour detection and segmentation algorithms, the BSDS500 provides a suite of evaluation measures. Please report all the scores and curves returned by the evaluation script boundaryBench (contour detection methods) or allBench(segmentation methods).
- Evaluate also on the BSDS300: In order to make your results comparable with methods that were evaluated on the original BSDS300, you should repeat the three steps above, but making sure to train only on the train subset of the data and test on the validation subset.
Download the BSDS500: images, ground-truth data and benchmarks.