RFBNet

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RFBNet is an open-source AI Agents & Automation tool for accurate and fast object detection. It introduces a novel Receptive Field Block (RFB) module to enhance feature discriminability.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is RFBNet?

RFBNet is an open-source Receptive Field Block Net designed for accurate and fast object detection, presented at ECCV 2018. Inspired by human visual systems, it proposes a novel RFB module that considers the relationship between the size and eccentricity of receptive fields to improve feature discriminability and robustness. The RFB module is integrated into SSD with a lightweight CNN model, forming the RFB Net detector. The repository provides code for training and evaluating RFB Net for object detection, supporting datasets like VOC and COCO. It offers various models including RFBNet300, RFBNet512, and RFB MobileNet, with pre-trained weights available for download. The tool is implemented in PyTorch and supports Python 3+.

Best used for

Ideal for developers and data scientists who need to implement advanced object detection, train custom models on VOC or COCO datasets, and evaluate model performance. Especially valuable for those seeking to enhance feature discriminability and robustness in computer vision applications.

Common actions

detect objects

train models

evaluate performance

implement computer vision

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Capabilities

Key features

Receptive Field Block module
Accurate object detection
Fast object detection
VGG16 backbone
MobileNet support
VOC/COCO dataset compatibility

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is the core innovation of RFBNet for object detection?

RFBNet introduces a novel Receptive Field Block (RFB) module, inspired by human visual systems. This module considers the relationship between the size and eccentricity of receptive fields to significantly enhance the discriminability and robustness of features, leading to more accurate and faster object detection.

What are the system requirements for running RFBNet?

RFBNet primarily supports PyTorch-0.4.0 and Python 3+. Users also need to install OpenCV and ensure their GPU architecture is supported, typically requiring CUDA capabilities. The repository provides a `make.sh` script for compiling necessary components like nms and coco tools.

Which datasets are supported for training and evaluation with RFBNet?

RFBNet supports both VOC (PASCAL VOC 2007 and 2012) and COCO datasets for training and evaluation. The repository includes scripts and instructions for downloading and preparing these datasets, making it compatible with the torchvision.datasets API for easy integration.

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