OmniCity-v1.0

Introduction

This project is used in Multi-level task based on MMDetection, which includes models and correlative configs.
Note： If you want to check more about our work, please refer here.

Overview of the models and its related tasks

Tasks: Instance segmentation, Land use segmentation, Plane segmentation.

Satellite level: small view

Data Type	Size	Method	Task	Download
Satallite image	512*512	Mask R-CNN	Instance segmentation	Model&log
Satallite image	512*512	MS R-CNN	Instance segmentation	Model&log
Satallite image	512*512	CARAFE	Instance segmentation	Model&log
Satallite image	512*512	Cascade	Instance segmentation	Model&log
Satallite image	512*512	HTC	Instance segmentation	Model&log

Satellite level: medium view

Data Type	Size	Method	Task	Download
Satallite image	512*512	Mask R-CNN	Instance segmentation	Model&log

Satellite level: large view

Data Type	Size	Method	Task	Download
Satellite image	512*512	Msak R-CNN	Instance segmentation	Model&log

Street level: Panorama

Data Type	Size	Method	Task	Download
Panorama image	512*1024	Mask R-CNN	Instance segmentation	Model&log
Panorama image	512*1024	Mask R-CNN	Land use segmentation	Model&log
Panorama image	512*1024	MS R-CNN	Land use segmentation	Model&log
Panorama image	512*1024	CARAFE	Land use segmentation	Model&log
Panorama image	512*1024	Cascade	Land use segmentation	Model&log
Panorama image	512*1024	HTC	Land use segmentation	Model&log

Street level: Mono-view

Data Type	Size	Method	Task	Download
Mono-view image	512*512	Mask R-CNN	Instance segmentation	Model&log
Mono-view image	512*512	Mask R-CNN	Land use segmentation	Model&log
Mono-view image	512*512	Mask R-CNN	Plane segmentation	Model&log

Usage

Data preparation

The OmniCity dataset can be downloaded from https://opendatalab.com/OmniCity.

If you want to use your own dataset test the models above, please prepare data following MMdetection(Dataset in COCO format is preferred). And the data structure should look like below:

mmdetection
├── data
│   ├── coco
│   │   ├── annotations
│   │   ├── train2017
│   │   ├── val2017
│   │   ├── test2017

Model test

With OmniCity dataset:

# single-gpu testing
python tools/test.py \
    ${CONFIG_FILE} \
    ${CHECKPOINT_FILE} \
    [--out ${RESULT_FILE}] \
    [--eval ${EVAL_METRICS}] \
    [--show]

# CPU: disable GPUs and run single-gpu testing script
export CUDA_VISIBLE_DEVICES=-1
python tools/test.py \
    ${CONFIG_FILE} \
    ${CHECKPOINT_FILE} \
    [--out ${RESULT_FILE}] \
    [--eval ${EVAL_METRICS}] \
    [--show]

# multi-gpu testing
bash tools/dist_test.sh \
    ${CONFIG_FILE} \
    ${CHECKPOINT_FILE} \
    ${GPU_NUM} \
    [--out ${RESULT_FILE}] \
    [--eval ${EVAL_METRICS}]

With new dataset:

• Prepare the dataset following the above rules
• Refer to the preceding operations