GOATmessi8/RFBNet
A PyTorch implementation of Receptive Field Block Net for real-time object detection, achieving 82.2 mAP on COCO at 38 FPS.
Not currently ranked — collecting fresh signals.
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This repository implements RFB Net, an object detection model that uses Receptive Field Block (RFB) modules inspired by human visual systems to enhance feature discriminability. Built on top of SSD with lightweight backbone networks, it provides efficient single-shot detection with competitive accuracy. The implementation supports training and evaluation on standard detection benchmarks including VOC and COCO datasets.
Frequently asked
- What is GOATmessi8/RFBNet?
- A PyTorch implementation of Receptive Field Block Net for real-time object detection, achieving 82.2 mAP on COCO at 38 FPS.
- Is RFBNet open source?
- Yes — GOATmessi8/RFBNet is open source, released under the MIT license.
- What language is RFBNet written in?
- GOATmessi8/RFBNet is primarily written in Python.
- How popular is RFBNet?
- GOATmessi8/RFBNet has 1.4k stars on GitHub.
- Where can I find RFBNet?
- GOATmessi8/RFBNet is on GitHub at https://github.com/GOATmessi8/RFBNet.