Semantic Segmentation
Pixel-level classification of images (Cityscapes, ADE20K).
Semantic Segmentation is a key task in computer vision. Below you will find the standard benchmarks used to evaluate models, along with current state-of-the-art results.
Benchmarks & SOTA
ADE20K
ADE20K Scene Parsing Benchmark
20K training, 2K validation images annotated with 150 object categories. Complex scene parsing benchmark.
State of the Art
InternImage-H
Shanghai AI Lab
62.9
mIoU
Cityscapes
Cityscapes Dataset
5,000 images with fine annotations and 20,000 with coarse annotations of urban street scenes.
No results tracked yet
Related Tasks
General OCR Capabilities
Comprehensive benchmarks covering multiple aspects of OCR performance.
Polish OCR
OCR for Polish language including historical documents, gothic fonts, and diacritic recognition.
Image Classification
Categorizing images into predefined classes (ImageNet, CIFAR).
Object Detection
Locating and classifying objects in images (COCO, Pascal VOC).