Object Detection Models
R-CNN
Introduced by Girshick et al. in Rich feature hierarchies for accurate object detection and semantic segmentationR-CNN, or Regions with CNN Features, is an object detection model that uses high-capacity CNNs to bottom-up region proposals in order to localize and segment objects. It uses selective search to identify a number of bounding-box object region candidates (“regions of interest”), and then extracts features from each region independently for classification.
Source: Rich feature hierarchies for accurate object detection and semantic segmentation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Object Detection | 16 | 20.00% |
| Object | 13 | 16.25% |
| Human Detection | 3 | 3.75% |
| General Classification | 3 | 3.75% |
| Autonomous Vehicles | 2 | 2.50% |
| Classification | 2 | 2.50% |
| Pose Estimation | 2 | 2.50% |
| Handwriting Recognition | 1 | 1.25% |
| Handwritten Text Recognition | 1 | 1.25% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
Convolution
|
Convolutions | |
Max Pooling
|
Pooling Operations | |
SVM
|
Non-Parametric Classification |
