Image Models
ProxylessNet-GPU
Introduced by Cai et al. in ProxylessNAS: Direct Neural Architecture Search on Target Task and HardwareProxylessNet-GPU is a convolutional neural network architecture learnt with the ProxylessNAS neural architecture search algorithm that is optimized for GPU devices. It uses inverted residual blocks (MBConvs) from MobileNetV2 as its basic building block.
Source: ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Classification | 2 | 40.00% |
| Few-Shot Image Classification | 1 | 20.00% |
| General Classification | 1 | 20.00% |
| Language Modelling | 1 | 20.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
Convolution
|
Convolutions | |
Global Average Pooling
|
Pooling Operations | |
Inverted Residual Block
|
Skip Connection Blocks |
