Feature Pyramid Blocks
Exact Fusion Model
Introduced by Wang et al. in CSPNet: A New Backbone that can Enhance Learning Capability of CNNExact Fusion Model (EFM) is a method for aggregating a feature pyramid. The EFM is based on YOLOv3, which assigns exactly one bounding-box prior to each ground truth object. Each ground truth bounding box corresponds to one anchor box that surpasses the threshold IoU. If the size of an anchor box is equivalent to the field-of-view of the grid cell, then for the grid cells of the $s$-th scale, the corresponding bounding box will be lower bounded by the $(s − 1)$th scale and upper bounded by the (s + 1)th scale. Therefore, the EFM assembles features from the three scales.
Source: CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Classification | 1 | 33.33% |
| Object Detection | 1 | 33.33% |
| Real-Time Object Detection | 1 | 33.33% |
Maxout
