Installation from Source

# Clone the repository
git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection

# Create and activate a conda environment
conda create -n openmmlab python=3.8 -y
conda activate openmmlab

# Install PyTorch (adjust according to your CUDA version)
pip install torch torchvision torchaudio

# Install MMDetection from source
pip install -v -e .

# Install additional dependencies
pip install mmengine mmcv

Running MAP Challenge Instance Segmentation Experiments

Project Configuration

You can find and edit experiment configurations in the projects/mapchallenge folder. Typical configurations will include:

• Model architectures
• Dataset settings
• Training parameters

Experiment Training Script

# Run the experiments script
bash train.sh

Test

python tools/test.py ${CONFIG_FILE from trainng} ${CHECKPOINT_FILE}  [--show] [--show-dir ${SHOW_DIR}]

MapChallenge Instance Segmentation Results

Model	segm mAP	segm mAP@50	segm mAP@75	segm mAP_s	segm mAP_m	segm mAP_l	segm mAR	segm mAR@50	segm mAR@75
SwinS-Mask2former	0.380	0.721	0.382	0.152	0.495	0.692	0.504	0.879	0.509
SwinL-Mask2former	0.406	0.695	0.427	0.202	0.521	0.624	0.529	0.845	0.569
RTMdet-M	0.403	0.726	0.452	0.186	0.523	0.737	0.527	0.888	0.586
RTMdet-X	0.418	0.743	0.472	0.194	0.536	0.769	0.529	0.897	0.586
QueryInst-r50	0.400	0.711	0.437	0.188	0.517	0.657	0.553	0.905	0.595
QueryInst-r101	0.406	0.689	0.44	0.184	0.524	0.665	0.581	0.914	0.655
MaskDINO	0.584	0.902	0.615	0.367	0.691	0.929	0.680	0.957	0.750

MapChallenge results at >= @0.50 IOU

Model	segm mAP@50	segm mAR@50
SwinS-Mask2former	0.721	0.879
SwinL-Mask2former	0.695	0.845
RTMdet-M	0.726	0.888
RTMdet-X	0.743	0.897
QueryInst-r50	0.711	0.905
QueryInst-r101	0.689	0.914
MaskDINO	0.902	0.957

Note:

• mAP@50/mAR@50 refers to average precision/recall at 0.50 and mAP@75/mAR@75 refers to average precision/recall at 0.75 IOU
• segm mAP values are usually averaged over multiple IOUs from 0.5 to 0.95 with interval of 0.05
• In metric columns, - s = small objects - m = medium objects - l = large objects

Related Repositories

For MaskDINO experiments, you might want to check out:

• MaskDINO training Repository