Datasets

Binaries/Code

Datasets

Open Source Software

		Freiburg-Berkeley Motion Segmentation Dataset			Video Segmentation Benchmark
		Image Sequences			TEM Dataset
		TILDA Textile Texture Database			Training data for Exemplar CNN
		Generated Matching Dataset			Training data for chair generation
		Stereo Ego-motion Dataset			Optical Flow Datasets: "Flying Chairs", "ChairsSDHom"
		Scene Flow Datasets			Human Part Segmentation Datasets
		Rendered Handpose Dataset			Pedestrian Zone Scene
		FreiHAND Dataset			HanCo Dataset
		Human Pose RGBD Datasets			OVAD: Open-Vocabulary Attribute Detection Dataset
		ADE-OoD: a benchmark for dense Out-of-Distribution detection on natural images.

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ADE-OoD Benchmark

ADE-OoD is a benchmark for dense Out-of-Distribution detection on general natural images. The goal of the benchmark is to extend the domain in-distribution and out-of-distribution beyond the common road scenes paradigm. To this end, the in-distribution ontology considered by ADE-OoD are the 150 categories ofthe ADE20k dataset for segmentic segmentation.

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