Methods › Computer Vision › Semantic Segmentation Models
Semantic Segmentation Models
The archive attaches this collection's text per method and the copies differ: 3 distinct texts across 32 of the 33 methods here. All are shown, most-carried first (a tie goes to the text carrying Papers with Code's collection boilerplate, then to the longer text); no vote is taken between them.
Text 1, carried by 30 of 33 methods:
Semantic Segmentation Models are a class of methods that address the task of semantically segmenting an image into different object classes. Below you can find a continuously updating list of semantic segmentation models.
Text 2, carried by 1 of 33 methods:
One-Stage Object Detection Models refer to a class of object detection models which are one-stage, i.e. models which skip the region proposal stage of two-stage models and run detection directly over a dense sampling of locations. These types of model usually have faster inference (possibly at the cost of performance). Below you can find a continuously updating list of one-stage object detection models.
Text 3, carried by 1 of 33 methods:
Object Detection Models are architectures used to perform the task of object detection. Below you can find a continuously updating list of object detection models.
Also reached at /methods/category/segmentation-models (Papers with Code's slug for this collection; the archive carries no slugs, so this site's is derived from the name).
Methods
All 33 methods in this collection, most-tagged first. Year is the archive's introduced_year; the archive stores 2000 when it has none, shown here as “–”. Papers counts distinct papers the archive tags with the method. Click a heading to sort.
| U-Net | – | 2,588 |
| FCN Fully Convolutional Network | – | 285 |
| SegNet | – | 81 |
| UNet++ | – | 61 |
| DeepLab | – | 55 |
| DeepLabv3 | – | 53 |
| PSPNet | – | 47 |
| SegFormer | – | 47 |
| EfficientDet | – | 38 |
| ENet | – | 24 |
| CABiNet Context Aggregated Bi-lateral Network for Semantic Segmentation | – | 23 |
| ESPNet | – | 23 |
| CCNet Criss-Cross Network | – | 6 |
| K-Net | – | 6 |
| HyperDenseNet | – | 5 |
| nnFormer | – | 5 |
| BASNet Boundary-Aware Segmentation Network | – | 4 |
| DeepLabv2 | – | 4 |
| UCTransNet | – | 4 |
| SETR Segmentation Transformer | – | 3 |
| ADELE Adaptive Early-Learning Correction | – | 2 |
| BiSeNet V2 | – | 2 |
| CascadePSP | – | 2 |
| DGRF Difference of Gaussian Random Forest | – | 2 |
| GenSAM Generalizable SAM | – | 2 |
| YOLOP | – | 2 |
| EdgeFlow | – | 1 |
| EfficientUNet++ | – | 1 |
| Error Back Proragation Network Bidirectional Pyramid Networks for Semantic Segmentation | – | 1 |
| IkshanaNet The Ikshana Hypothesis of Human Scene Understanding Mechanism | – | 1 |
| LiteSeg | – | 1 |
| O-Net | – | 1 |
| PSANet | – | 1 |