Methods › Computer Vision › One-Stage Object Detection Models
One-Stage Object Detection Models
The archive attaches this collection's text per method and the copies differ: 2 distinct texts across 21 of the 21 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 18 of 21 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.
Text 2, carried by 3 of 21 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.
Methods
All 21 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.
| SSD | – | 278 |
| YOLOv3 | – | 258 |
| RetinaNet | – | 209 |
| YOLOv4 | – | 100 |
| FCOS | – | 80 |
| YOLOv2 | – | 55 |
| CenterNet | – | 47 |
| EfficientDet | – | 38 |
| YOLOX | – | 33 |
| CPN Contour Proposal Network | – | 14 |
| CornerNet | – | 7 |
| YOLOv1 | – | 6 |
| FoveaBox | – | 4 |
| PP-YOLO | – | 4 |
| ExtremeNet | – | 2 |
| M2Det | – | 2 |
| RFB Net | – | 2 |
| RetinaNet-RS | – | 2 |
| YOLOP | – | 2 |
| CornerNet-Saccade | – | 1 |
| RetinaMask | – | 1 |