Methods › Computer Vision › Object Detection Models
Object Detection Models
The archive attaches this collection's text per method and the copies differ: 3 distinct texts across 62 of the 63 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 59 of 63 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 2 of 63 methods:
Instance Segmentation models are models that perform the task of Instance Segmentation.
Text 3, carried by 1 of 63 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 63 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.
| Faster R-CNN | – | 499 |
| Mask R-CNN | – | 420 |
| SSD | – | 278 |
| YOLOv3 | – | 258 |
| YOLOv8 You Only Look Once | – | 254 |
| Detr Detection Transformer | – | 222 |
| RetinaNet | – | 209 |
| YOLO You Only Look Once | – | 108 |
| YOLOv4 | – | 100 |
| FCOS | – | 80 |
| YOLOv2 | – | 55 |
| CenterNet | – | 47 |
| EfficientDet | – | 38 |
| Fast R-CNN | – | 38 |
| Deformable DETR | – | 35 |
| Cascade R-CNN | – | 34 |
| R-CNN | – | 34 |
| R-FCN Region-based Fully Convolutional Network | – | 32 |
| PANet | – | 18 |
| Sparse R-CNN | – | 16 |
| CPN Contour Proposal Network | – | 14 |
| MDETR | – | 13 |
| GCNet | – | 11 |
| RepPoints | – | 11 |
| VFNet VarifocalNet | – | 8 |
| CornerNet | – | 7 |
| SASA Stand-Alone Self Attention | – | 7 |
| YOLOv1 | – | 6 |
| Libra R-CNN | – | 5 |
| RTMDet RTMDet: An Empirical Study of Designing Real-Time Object Detectors | – | 5 |
| FoveaBox | – | 4 |
| Grid R-CNN | – | 4 |
| HTCN Hierarchical Transferability Calibration Network | – | 4 |
| PP-YOLO | – | 4 |
| ThunderNet | – | 4 |
| Dynamic R-CNN | – | 3 |
| TridentNet | – | 3 |
| U2-Net | – | 3 |
| CoVA Context-aware Visual Attention-based (CoVA) webpage object detection pipeline | – | 2 |
| DAFNe | – | 2 |
| ExtremeNet | – | 2 |
| M2Det | – | 2 |
| MobileDet | – | 2 |
| NAS-FCOS | – | 2 |
| PP-YOLOv2 | – | 2 |
| RFB Net | – | 2 |
| RPDet | – | 2 |
| SABL Side-Aware Boundary Localization | – | 2 |
| YOLOP | – | 2 |
| C3-AMP Amplified Cross-Stage Partial Block | – | 1 |
| CentripetalNet | – | 1 |
| CornerNet-Saccade | – | 1 |
| CornerNet-Squeeze | – | 1 |
| DAMO-YOLO | – | 1 |
| F2DNet Fast Focal Detection Network | – | 1 |
| H3DNet | – | 1 |
| MutualGuide Mutual Guidance | – | 1 |
| PAFNet Paddle Anchor Free Network | – | 1 |
| PFPNet Parallel Feature Pyramid Network | – | 1 |
| RetinaMask | – | 1 |
| SPPF-AMP Amplified Spatial Pyramid Pooling Fast | – | 1 |
| ScanSSD | – | 1 |
| HRI pipeline Human Robot Interaction Pipeline | – | 0 |