PeleeNet

Introduced by Wang et al. in Pelee: A Real-Time Object Detection System on Mobile Devices

PeleeNet is a convolutional neural network and object detection backbone that is a variation of DenseNet with optimizations to meet a memory and computational budget. Unlike competing networks, it does not use depthwise convolutions and instead relies on regular convolutions.

Source: Pelee: A Real-Time Object Detection System on Mobile Devices

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Papers

Paper	Code	Results	Date	Stars

Tasks

Task	Papers	Share
Object Detection	2	25.00%
Real-Time Object Detection	2	25.00%
Computational Efficiency	1	12.50%
Emotion Recognition	1	12.50%
Facial Expression Recognition (FER)	1	12.50%
Image Classification	1	12.50%

Usage Over Time

This feature is experimental; we are continuously improving our matching algorithm.

Components

Component	Type	Add Remove
1x1 Convolution	Convolutions
Average Pooling	Pooling Operations
Batch Normalization	Normalization
Bottleneck Residual Block	Skip Connection Blocks
Convolution	Convolutions
Dense Connections	Feedforward Networks
Global Average Pooling	Pooling Operations
Max Pooling	Pooling Operations
ReLU	Activation Functions
Softmax	Output Functions
Two-Way Dense Layer	Skip Connection Blocks

Categories

Add Remove

Convolutional Neural Networks

Light-weight neural networks