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 Script

# Run the experiments script
bash train.sh

MAP Challenge 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	0.3110	0.7160	0.2180	0.1690	0.4420	0.0990	0.42	0.833	0.362
SwinL-Mask2former	0.3080	0.7260	0.2360	0.1710	0.4400	0.1310	0.453	0.855	0.464
Rtmdet-X	0.3910	0.7760	0.3440	0.2090	0.5440	0.2930	0.509	0.891	0.5
RTMdet-M	0.3790	0.7540	0.3170	0.2150	0.5210	0.2820	0.485	0.87	0.457
QueryInst-r50	0.2770	0.6520	0.1830	0.1380	0.3890	0.1530	0.432	0.826	0.384
QueryInst-r101	0.2780	0.6340	0.2000	0.1760	0.3910	0.1010	0.458	0.855	0.464
MaskDINO	0.584	0.9022	0.6150	0.3670	0.6911	0.9287	0.6802	0.9569	0.75

MAP Challenge Results for 0.50 IoU

Model	segm mAP_50
SwinS-Mask2former	0.7160
SwinL-Mask2former	0.7260
Rtmdet-X	0.7760
RTMdet-M	0.7540
QueryInst-r50	0.6520
QueryInst-r101	0.6340
MaskDINO	0.9022

## Related Repositories

For MaskDINO experiments, you might want to check out:

• MaskDINO training Repository