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}
}| FaPN vs. FPN | Before vs. After Alignment |
|---|---|
 |
 |
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.
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.
To train a model with 8 GPUs, run:
cd /path/to/detectron2/tools
python3 train_net.py --config-file <config.yaml> --num-gpus 8For 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 8To 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| Name | lr sched |
box AP |
box APs |
box APm |
box APl |
download |
|---|---|---|---|---|---|---|
| R50 | 1x | 39.2 | 24.5 | 43.3 | 49.1 | model | log |
| R101 | 3x | 42.8 | 27.0 | 46.2 | 54.9 | model | log |
| Name | lr sched |
mask mIoU |
mask i_IoU |
mask IoU_sup |
mask iIoU_sup |
download |
|---|---|---|---|---|---|---|
| R50 | 1x | 80.0 | 61.3 | 90.6 | 78.5 | model | log |
| R101 | 1x | 80.1 | 62.2 | 90.8 | 78.6 | model | log |
| Name | lr sched |
mask AP |
mask APs |
box AP |
box APs |
download |
|---|---|---|---|---|---|---|
| R50 | 1x | 36.4 | 18.1 | 39.8 | 24.3 | model | log |
| R101 | 3x | 39.4 | 20.9 | 43.8 | 27.4 | model | log |
| Name | lr sched |
mask AP |
mask APs |
box AP |
box APs |
download |
|---|---|---|---|---|---|---|
| R50 | 1x | 37.6 | 18.6 | 39.4 | 24.2 | model | log |
| Name | lr sched |
PQ | mask mIoU |
St PQ |
box AP |
Th PQ |
download |
|---|---|---|---|---|---|---|---|
| R50 | 1x | 41.1 | 43.4 | 32.5 | 38.7 | 46.9 | model | log |
| R101 | 3x | 44.2 | 45.7 | 35.0 | 43.0 | 53.3 | model | log |
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