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https://github.com/uber-research/UPSNet
UPSNet: A Unified Panoptic Segmentation Network
https://github.com/uber-research/UPSNet
computer-vision cvpr2019 deep-learning instance-segmentation panoptic-segmentation scene-parsing
Last synced: about 1 month ago
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UPSNet: A Unified Panoptic Segmentation Network
- Host: GitHub
- URL: https://github.com/uber-research/UPSNet
- Owner: uber-research
- License: other
- Created: 2019-03-02T00:44:18.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2019-07-11T21:59:54.000Z (over 5 years ago)
- Last Synced: 2024-08-01T20:36:46.550Z (4 months ago)
- Topics: computer-vision, cvpr2019, deep-learning, instance-segmentation, panoptic-segmentation, scene-parsing
- Language: Python
- Homepage:
- Size: 164 KB
- Stars: 642
- Watchers: 28
- Forks: 119
- Open Issues: 76
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-computer-vision-papers - 2019CVPR
README
# UPSNet: A Unified Panoptic Segmentation Network
# Introduction
UPSNet is initially described in a [CVPR 2019 oral](https://arxiv.org/abs/1901.03784) paper.
# Disclaimer
This repository is tested under Python 3.6, PyTorch 0.4.1. And model training is done with 16 GPUs by using [horovod](https://github.com/horovod/horovod). It should also work under Python 2.7 / PyTorch 1.0 and with 4 GPUs.
# License
© Uber, 2018-2019. Licensed under the Uber Non-Commercial License.
# Citing UPSNet
If you find UPSNet is useful in your research, please consider citing:
```
@inproceedings{xiong19upsnet,
Author = {Yuwen Xiong, Renjie Liao, Hengshuang Zhao, Rui Hu, Min Bai, Ersin Yumer, Raquel Urtasun},
Title = {UPSNet: A Unified Panoptic Segmentation Network},
Conference = {CVPR},
Year = {2019}
}
```
# Main Results
COCO 2017 (trained on train-2017 set)
| | test split | PQ | SQ | RQ | PQTh | PQSt |
|----------------|------------|------|------|------|-----------------|-----------------|
| UPSNet-50 | val | 42.5 | 78.0 | 52.4 | 48.5 | 33.4 |
| UPSNet-101-DCN | test-dev | 46.6 | 80.5 | 56.9 | 53.2 | 36.7 |
Cityscapes
| | PQ | SQ | RQ | PQTh | PQSt |
|----------------|------|------|------|-----------------|-----------------|
| UPSNet-50 | 59.3 | 79.7 | 73.0 | 54.6 | 62.7 |
| UPSNet-101-COCO (ms test) | 61.8 | 81.3 | 74.8 | 57.6 | 64.8 |
# Requirements: Software
We recommend using Anaconda3 as it already includes many common packages.
# Requirements: Hardware
We recommend using 4~16 GPUs with at least 11 GB memory to train our model.
# Installation
Clone this repo to `$UPSNet_ROOT`
Run `init.sh` to build essential C++/CUDA modules and download pretrained model.
For Cityscapes:
Assuming you already downloaded Cityscapes dataset at `$CITYSCAPES_ROOT` and TrainIds label images are generated, please create a soft link by `ln -s $CITYSCAPES_ROOT data/cityscapes` under `UPSNet_ROOT`, and run `init_cityscapes.sh` to prepare Cityscapes dataset for UPSNet.
For COCO:
Assuming you already downloaded COCO dataset at `$COCO_ROOT` and have `annotations` and `images` folders under it, please create a soft link by `ln -s $COCO_ROOT data/coco` under `UPSNet_ROOT`, and run `init_coco.sh` to prepare COCO dataset for UPSNet.
Training:
`python upsnet/upsnet_end2end_train.py --cfg upsnet/experiments/$EXP.yaml`
Test:
`python upsnet/upsnet_end2end_test.py --cfg upsnet/experiments/$EXP.yaml`
We provide serveral config files (16/4 GPUs for Cityscapes/COCO dataset) under upsnet/experiments folder.
# Model Weights
The model weights that can reproduce numbers in our paper are available now. Please follow these steps to use them:
Run `download_weights.sh` to get trained model weights for Cityscapes and COCO.
For Cityscapes:
```shell
python upsnet/upsnet_end2end_test.py --cfg upsnet/experiments/upsnet_resnet50_cityscapes_16gpu.yaml --weight_path ./model/upsnet_resnet_50_cityscapes_12000.pth
```
```shell
python upsnet/upsnet_end2end_test.py --cfg upsnet/experiments/upsnet_resnet101_cityscapes_w_coco_16gpu.yaml --weight_path ./model/upsnet_resnet_101_cityscapes_w_coco_3000.pth
```
For COCO:
```shell
python upsnet/upsnet_end2end_test.py --cfg upsnet/experiments/upsnet_resnet50_coco_16gpu.yaml --weight_path model/upsnet_resnet_50_coco_90000.pth
```
```shell
python upsnet/upsnet_end2end_test.py --cfg upsnet/experiments/upsnet_resnet101_dcn_coco_3x_16gpu.yaml --weight_path model/upsnet_resnet_101_dcn_coco_270000.pth
```