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FaPN: Feature-aligned Pyramid Network for Dense Image Prediction [arXiv] [Project Page]

@inproceedings{
  huang2021fapn,
  title={{FaPN}: Feature-aligned Pyramid Network for Dense Image Prediction},
  author={Shihua Huang and Zhichao Lu and Ran Cheng and Cheng He},
  booktitle={International Conference on Computer Vision (ICCV)},
  year={2021}
}

Overview

This project provides the official implementation for our ICCV2021 paper "FaPN: Feature-aligned Pyramid Network for Dense Image Prediction" based on Detectron2. FaPN is a simple yet effective top-down pyramidal architecture to generate multi-scale features for dense image prediction. Comprised of a feature alignment module (FAM) and a feature selection module (FSM), FaPN addresses the issue of feature alignment in the original FPN, leading to substaintial improvements on various dense prediction tasks, such as object detection, semantic, instance, panoptic segmentation, etc.

Installation

This project is based on Detectron2, which can be constructed as follows.

• Install Detectron2 following the instructions.
• Setup the dataset following the structure.
• Copy this project to /path/to/detectron2
• Install DCNv2 following Install DCNv2.md.

Training

To train a model with 8 GPUs, run:

cd /path/to/detectron2/tools
python3 train_net.py --config-file <config.yaml> --num-gpus 8

For example, to launch Faster R-CNN training (1x schedule) with ResNet-50 backbone on 8 GPUs, one should execute:

cd /path/to/detectron2/tools
python3 train_net.py --config-file ../configs\COCO-Detection\faster_rcnn_R_50_FAN_1x.yaml --num-gpus 8

Evaluation

To evaluate a pre-trained model with 8 GPUs, run:

cd /path/to/detectron2/tools
python3 train_net.py --config-file <config.yaml> --num-gpus 8 --eval-only MODEL.WEIGHTS /path/to/model_checkpoint

Results

COCO Object Detection

Faster R-CNN + FaPN:

Cityscapes Semantic Segmentation

PointRend + FaPN:

COCO Instance Segmentation

Mask R-CNN + FaPN:

PointRend + FaPN:

COCO Panoptic Segmentation

PanopticFPN + FaPN: