Object Detection Models
PP-YOLO
Introduced by Long et al. in PP-YOLO: An Effective and Efficient Implementation of Object DetectorPP-YOLO is an object detector based on YOLOv3. It mainly tries to combine various existing tricks that almost not increase the number of model parameters and FLOPs, to achieve the goal of improving the accuracy of detector as much as possible while ensuring that the speed is almost unchanged. Some of these changes include:
- Changing the DarkNet-53 backbone with ResNet50-vd. Some of the convolutional layers in ResNet50-vd are also replaced with deformable convolutional layers.
- A larger batch size is used - changing from 64 to 192.
- An exponentially moving average is used for the parameters.
- DropBlock is applied to the FPN.
- An IoU loss is used.
- An IoU prediction branch is added to measure the accuracy of localization.
- Grid Sensitive is used, similar to YOLOv4.
- Matrix NMS is used.
- CoordConv is used for the FPN, replacing the 1x1 convolution layer, and also the first convolution layer in the detection head.
- Spatial Pyramid Pooling is used for the top feature map.
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Object Detection | 2 | 18.18% |
| Real-Time Object Detection | 2 | 18.18% |
| Object | 2 | 18.18% |
| 2D Object Detection | 1 | 9.09% |
| Dense Object Detection | 1 | 9.09% |
| Multi-Object Tracking | 1 | 9.09% |
| Multiple Object Tracking | 1 | 9.09% |
| Online Multi-Object Tracking | 1 | 9.09% |
CoordConv
Deformable Convolution
DropBlock
FPN
Grid Sensitive
ResNet
Spatial Pyramid Pooling
