r fcn

We have hosted the application r fcn in order to run this application in our online workstations with Wine or directly.

Quick description about r fcn:

R-FCN (“Region-based Fully Convolutional Networks”) is an object detection framework that makes almost all computation fully convolutional and shared across the image, unlike prior region-based approaches (e.g. Faster R-CNN) which run per-region sub-networks. The repository provides an implementation (in Python) supporting end-to-end training and inference of R-FCN models on standard datasets. The authors propose position-sensitive score maps to reconcile the need for translation variance (in detection) and translation invariance (in classification). R-FCN is efficient (low per-region overhead) and competitive in accuracy (e.g. with ResNet backbones).

Features:

• Fully convolutional design with shared feature extraction across the image
• Position-sensitive score maps for per-region classification without expensive per-region convs
• End-to-end trainable pipeline (proposal + classification)
• Support for multiple backbone architectures (e.g. ResNet)
• Optional “deformable R-FCN” extension for improved performance
• Low per-RoI overhead (fast inference)

Programming Language: MATLAB.
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