Fast R-CNN is an object detection model that improves in its predecessor R-CNN in a number of ways. Instead of extracting CNN features independently for each region of interest, Fast R-CNN aggregates them into a single forward pass over the image; i.e. regions of interest from the same image share computation and memory in the forward and backward passes.
Source: Fast R-CNNPaper | Code | Results | Date | Stars |
---|
Task | Papers | Share |
---|---|---|
Object Detection | 23 | 27.38% |
Object | 16 | 19.05% |
General Classification | 4 | 4.76% |
Classification | 3 | 3.57% |
Real-Time Object Detection | 3 | 3.57% |
Object Recognition | 3 | 3.57% |
Weakly Supervised Object Detection | 2 | 2.38% |
Autonomous Driving | 2 | 2.38% |
Autonomous Vehicles | 2 | 2.38% |
Component | Type |
|
---|---|---|
Convolution
|
Convolutions | |
RoIPool
|
RoI Feature Extractors | |
Softmax
|
Output Functions |