Chanllenge results: https://monuseg.grand-challenge.org/Results/
Slides: https://docs.google.com/presentation/d/1jS9YEs_KVBamoYdEZ0oSGUbIBQmr2htOz12dQLdf4Sk/edit?usp=sharing
Manuscript: https://drive.google.com/open?id=1S1apR4SV_aCiFbfLCaAkhh3EpJCfDCDu
Please install package below
pip install numba numexpr pygsheets oauth2client
First, setup your model hyper-parameter config in the monuconfig.py. We support backone: resnet50/101, densenet121/169 and inception-resnetv2, please set the model in BACKBONE.
class Config(object):
NAME = "name your model"
RPN_ANCHOR_SCALE = (2, 4, 6, 8, 10)
BACKBONE = "resnet101"
...
Now support Path Aggregation Network and used as default. If you want to use original Mask-RCNN, please revise code in train.py when creating model
# Create model
model = modellib.MaskRCNN(mode="training", config=config,
model_dir=args.logs, is_PANet=False)
Then train the Mask-RCNN by
python train.py --weight imagenet --dataset dataset/ --logs logs/ --subset train
Already implemented features
- Path Aggregation Network
- Speed up data generator by
- feed all data into memory first
- apply Numba on utils.compute_overlap
- rewrite utils.extract_boxex
- revise some indexing code
- Support more pre-train model structure like DenseNet, Inception-Resnetv2
- Config and AJI results will be automatically recored on gsheets
- Speed up AJI code (implemented by 旻昇, 友誠)
TODO
- Synchronize Batch Normalization
- soft-NMS
- relation network
- Attetion on FPN