├── code
│ ├── configs
│ ├── crop_det.py
│ ├── make_submission.py
│ ├── mmdetection
│ ├── prepare_det.py
│ └── work_dirs
├── data
│ ├── dtrainval.pkl
│ ├── train_images
│ ├── test_images
│ ├── train_crops
│ └── ...
├── download
│ ├── test_images.zip
│ ├── train_images.zip
│ └── ...
└── submits
├── submit001.csv
└── ...
- 2x 1080 Ti
- mmdetection with a few modifications (e.g. mmdetection/mmdet/datasets/pipelines/test_aug.py)
- requirements.txt
The solution is straightfoward.
Cascade R-CNN with:
- Strong backbones
- Multi-scale train&test
Due to limited GPU memory, models were trained on 1024x1024 crops and tested on full images(with a max size limit).
LB score 0.935 with:
- HRNet w32
- train scales 512~768
- test scales [0.5, 0.625, 0.75]
LB score 0.946 with:
- HRNet w32
- train scales 768~1280
- test scales [0.75, 0.875, 1.0, 1.125, 1.25]
Ensembling HRNet_w32 and HRNet_w48 results -> 0.950.
Install mmdetection following ./mmdetection/docs/INSTALL.md
Install packages following requirements.txt
python prepare_det.py
python crop_det.py
./mmdetection/tools/dist_train.sh configs/hr32.py 2 --seed 0
./mmdetection/tools/dist_test.sh configs/hr32.py work_dirs/hr32/epoch_12.pth 2 --out work_dirs/hr32/test_result.pkl
python make_submission.py ../submits/submit001.csv --input work_dirs/hr32/test_result.pkl
# python make_submission.py ../submits/submit001.csv --input work_dirs/hr32/test_result.pkl work_dirs/hr48/test_result.pkl
python inference.py configs/hr32.py ../data/kuzushiji-hrnetv2p_w32-f391e720.pth ../data/test_images/test_012f99f8.jpg ../data/out.jpg
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