TasksSotADatasetsPapersMethodsSubmitAbout
Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable BenchmarksAll SotADatasetsPapersMethods

Community

Submit ResultsAbout

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Datasets/CamVid

CamVid

Cambridge-driving Labeled Video Database

ImagesVideosUnknownIntroduced 2009-01-01

CamVid (Cambridge-driving Labeled Video Database) is a road/driving scene understanding database which was originally captured as five video sequences with a 960×720 resolution camera mounted on the dashboard of a car. Those sequences were sampled (four of them at 1 fps and one at 15 fps) adding up to 701 frames. Those stills were manually annotated with 32 classes: void, building, wall, tree, vegetation, fence, sidewalk, parking block, column/pole, traffic cone, bridge, sign, miscellaneous text, traffic light, sky, tunnel, archway, road, road shoulder, lane markings (driving), lane markings (non-driving), animal, pedestrian, child, cart luggage, bicyclist, motorcycle, car, SUV/pickup/truck, truck/bus, train, and other moving object

Source: A Review on Deep Learning TechniquesApplied to Semantic Segmentation Image Source: http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/

Benchmarks

10-shot image generation/Mean IoU10-shot image generation/Global Accuracy10-shot image generation/mIoU10-shot image generation/Frame (fps)10-shot image generation/Time (ms)2D Semantic Segmentation/mIoU2D Semantic Segmentation/Mean IoUScene Parsing/Mean IoUScene Understanding/Mean IoUSemantic Segmentation/Mean IoUSemantic Segmentation/Global AccuracySemantic Segmentation/mIoUSemantic Segmentation/Frame (fps)Semantic Segmentation/Time (ms)Video Semantic Segmentation/Mean IoU

Statistics

Papers
227
Benchmarks
15

Links

Homepage

Tasks

10-shot image generation2D Semantic SegmentationReal-Time Semantic SegmentationScene ParsingScene UnderstandingSemantic SegmentationVideo Semantic Segmentation