On-the-fly Network Pruning for Object Detection
Object detection with deep neural networks is often performed by passing a few thousand candidate bounding boxes through a deep neural network for each image. These bounding boxes are highly correlated since they originate from the same image. In this paper we investigate how to exploit feature occurrence at the image scale to prune the neural network which is subsequently applied to all bounding boxes. We show that removing units which have near-zero activation in the image allows us to significantly reduce the number of parameters in the network. Results on the PASCAL 2007 Object Detection Challenge demonstrate that up to 40% of units in some fully-connected layers can be entirely eliminated with little change in the detection result.
Code (0)
등록된 구현이 없습니다.
Tasks
Network PruningObjectobject-detectionObject DetectionSimilar Papers 제목 키워드 기반
Anchor Pruning for Object Detection
This paper proposes anchor pruning for object detection in one-stage anchor-based detectors. While pruning techniques are widely used to reduce the computational cost of convolutional neural networks, they tend to focus …
Objectobject-detectionObject DetectionAchieving Real-Time Object Detection on MobileDevices with Neural Pruning Search
Object detection plays an important role in self-driving cars for security development. However, mobile systems on self-driving cars with limited computation resources lead to difficulties for object detection. To facili…
2D Object Detection3D Object DetectionAutonomous VehiclesCompiler Optimization+5Localization-aware Channel Pruning for Object Detection
Channel pruning is one of the important methods for deep model compression. Most of existing pruning methods mainly focus on classification. Few of them conduct systematic research on object detection. However, object de…
ClassificationGeneral ClassificationModel CompressionObject+3Strawberry detection and counting based on YOLOv7 pruning and information based tracking algorithm
The strawberry industry yields significant economic benefits for Florida, yet the process of monitoring strawberry growth and yield is labor-intensive and costly. The development of machine learning-based detection and t…
Multiple Object TrackingObjectObject TrackingExplainability-Inspired Layer-Wise Pruning of Deep Neural Networks for Efficient Object Detection
Deep neural networks (DNNs) have achieved remarkable success in object detection tasks, but their increasing complexity poses significant challenges for deployment on resource-constrained platforms. While model compressi…
Model CompressionObject Detection