Feature Extractors
NAS-FPN
Introduced by Ghiasi et al. in NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object DetectionNAS-FPN is a Feature Pyramid Network that is discovered via Neural Architecture Search in a novel scalable search space covering all cross-scale connections. The discovered architecture consists of a combination of top-down and bottom-up connections to fuse features across scales
Source: NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Object Detection | 9 | 34.62% |
| Semantic Segmentation | 4 | 15.38% |
| Instance Segmentation | 3 | 11.54% |
| Image Augmentation | 2 | 7.69% |
| General Classification | 2 | 7.69% |
| Image Classification | 2 | 7.69% |
| Real-Time Object Detection | 2 | 7.69% |
| Point Cloud Registration | 1 | 3.85% |
| Robust Object Detection | 1 | 3.85% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
Batch Normalization
|
Normalization | |
Convolution
|
Convolutions | |
Global Average Pooling
|
Pooling Operations | |
Neural Architecture Search
|
Neural Architecture Search | |
ReLU
|
Activation Functions |
