zxd52csx / m2sodai

M²SODAI Dataset

1. Install Dependencies

1. Anaconda

conda create -n Maritime python=3.7

2. Pytorch

pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio===0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
conda install jupyterlab
jupyter server extension disable nbclassic

3. Other extra dependencies

pip install spectral matplotlib scikit-image fire openmim
pip install mmcv-full==1.3.17 -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7/index.html
# for instaboost
pip install instaboostfast
# for panoptic segmentation
pip install git+https://github.com/cocodataset/panopticapi.git
# for LVIS dataset
pip install git+https://github.com/lvis-dataset/lvis-api.git
# for albumentations
pip install -r requirements/albu.txt
pip install -r requirements/build.txt
pip install -v -e .  # or "python setup.py develop"
pip install labelme==4.5.13

2. Prepare Dataset

1. Download data

wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1vPReTPfYSLsKGUdrjqi0l_nCNDZyr5d6' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1vPReTPfYSLsKGUdrjqi0l_nCNDZyr5d6" -O m2sodai.zip && rm -rf /tmp/cookies.txt  

2. Unzip

unzip new.zip
rm new.zip
# use softlink 
ln -sf {source} {dest ex. data}

3. run data_tools/lableme2coco.py

python data_tools/labelme2coco.py data/test data/test_coco --label data/label.txt
python data_tools/labelme2coco.py data/train data/train_coco --label data/label.txt
python data_tools/labelme2coco.py data/val data/val_coco --label data/label.txt

3. How to RUN?

1. Check dataset

2. Compute the mean and variance of the data set.

python ./data_tools/mean_var_calculator.py
python ./data_tools/mean_var_calculator_jpg.py

3. Training

python tools/train.py {config_file}

4. Evaluation (test)

python tools/test.py {config_file} {output_file} --eval bbox --show-score-thr 0.5

Acknowledgment

• This work is forked from MMdetection Repository https://github.com/open-mmlab/mmdetection
• A useful instruction of Faster R-CNN: https://www.lablab.top/post/how-does-faster-r-cnn-work-part-ii/