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.
Tasks: Instance segmentation, Land use segmentation, Plane segmentation.
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 |
Data Type | Size | Method | Task | Download |
---|---|---|---|---|
Satallite image | 512*512 | Mask R-CNN | Instance segmentation | Model&log |
Data Type | Size | Method | Task | Download |
---|---|---|---|---|
Satellite image | 512*512 | Msak R-CNN | Instance segmentation | Model&log |
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 |
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 |
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
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