Segmentation / video segmentaion
- Classes : 38 groups of images
- Total images : 643 images (Approximately 17 images per group)
DAVIS: Densely Annotated VIdeo Segmentation
Image Segmentation with A Bounding Box Prior dataset
- Ground truth database of 50 images with: Data, Segmentation, Labelling - Lasso, Labelling - Rectangle
- Classification/Detection Competitions, Segmentation Competition, Person Layout Taster Competition datasets
Motion Segmentation and OBJCUT data
- Cows for object segmentation, Five video sequences for motion segmentation
- Geometric Context Dataset: pixel labels for seven geometric classes for 300 images
- This dataset contains videos of crowds and other high density moving objects. The videos are collected mainly from the BBC Motion Gallery and Getty Images website. The videos are shared only for the research purposes. Please consult the terms and conditions of use of these videos from the respective websites.
Segmentation evaluation database 200 gray level images along with ground truth segmentations
The Berkeley Segmentation Dataset and Benchmark
- Image segmentation and boundary detection. Grayscale and color segmentations for 300 images, the images are divided into a training set of 200 images, and a test set of 100 images.
- 328 side-view color images of horses that were manually segmented. The images were randomly collected from the WWW.
Saliency-based video segmentation with sequentially updated priors
- 10 videos as inputs, and segmented image sequences as ground-truth
Daimler Urban Segmentation Dataset
- The dataset consists of video sequences recorded in urban traffic. The dataset consists of 5000 rectified stereo image pairs. 500 frames come with pixel-level semantic class annotations into 5 classes: ground, building, vehicle, pedestrian, sky. Dense disparity maps are provided as a reference.
DAVIS: Densely Annotated VIdeo Segmentation 2016
- A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation.
DAVIS: Densely Annotated VIdeo Segmentation 2017
- The 2017 DAVIS Challenge on Video Object Segmentation.
- Object Co-Skeletonization With Co-Segmentation.
CITY-OSM: Learning Aerial Image Segmentation From Online Maps
- +20GB of aerial images obtained from Google Maps (including the groundtruth from OSM).
Mut1ny Face/Headsegmentation dataset
- Head/face segmentation dataset contains over 16k labeled images.
The Unsupervised LLAMAS dataset
- A lane marker detection and segmentation dataset of 100,000 images with 3d lines, pixel level dashed markers, and curves for individual lines.