mask-rcnn

This is a Pytorch 1.0 implementation of Mask R-CNN that is based on Matterport's Mask_RCNN[1] and this[2]. Matterport's repository is an implementation on Keras and TensorFlow.

The main improvements from [2] are:

• Pytorch 1.0
• most numpy computations were ported to pytorch (for GPU speed)
• supports batchsize > 1
• some bugs were fixed in the translation process
• code refactor
• NMS speed-up

Currently, it works with Kaggle's 2018 Data Science Bowl dataset (the result on 1st phase testset is 0.27).

to train the network use: python samples/nuclei.py train --dataset=path_to_dataset --model=coco

to detect use: python samples/nuclei.py submit --dataset=path_to_dataset --model=path_to_trained_model

to check Kaggle's 2018 Databowl metric on a dataset use: python samples/nuclei.py metric --dataset=path_to_dataset --model=path_to_trained_model

for installation instructions, just export mrcnn directory to PYTHONPATH and run: python setup.py install