Convolutional Neural Networks
DetNASNet
Introduced by Chen et al. in DetNAS: Backbone Search for Object DetectionDetNASNet is a convolutional neural network designed to be an object detection backbone and discovered through DetNAS architecture search. It uses ShuffleNet V2 blocks as its basic building block.
Source: DetNAS: Backbone Search for Object Detection
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| General Classification | 1 | 33.33% |
| Image Classification | 1 | 33.33% |
| Object Detection | 1 | 33.33% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
Batch Normalization
|
Normalization | |
Convolution
|
Convolutions | |
ReLU
|
Activation Functions | |
ShuffleNet V2 Block
|
Image Model Blocks |
