xvjiarui/GCNet
GCNet is a deep learning architecture combining non-local networks and squeeze-excitation blocks for object detection and instance segmentation tasks.
Not currently ranked — collecting fresh signals.
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This repository provides an official implementation of the GCNet paper on COCO object detection and instance segmentation. The core component is the Global Context (GC) block, a neural network operator that captures long-range dependencies in images. Built on the open-mmlab mmdetection framework, it provides configurations and trained models for reproducing the reported benchmarks.
Frequently asked
- What is xvjiarui/GCNet?
- GCNet is a deep learning architecture combining non-local networks and squeeze-excitation blocks for object detection and instance segmentation tasks.
- Is GCNet open source?
- Yes — xvjiarui/GCNet is open source, released under the Apache-2.0 license.
- What language is GCNet written in?
- xvjiarui/GCNet is primarily written in Python.
- How popular is GCNet?
- xvjiarui/GCNet has 1.2k stars on GitHub.
- Where can I find GCNet?
- xvjiarui/GCNet is on GitHub at https://github.com/xvjiarui/GCNet.