The code is documented and designed to be easy to extend. Example of training on your own dataset.ParallelModel class for multi-GPU training.Jupyter notebooks to visualize the detection pipeline at every step.Source code of Mask R-CNN built on FPN and ResNet101.It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone. The model generates bounding boxes and segmentation masks for each instance of an object in the image. This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. Mask R-CNN for Object Detection and Segmentation * E999: Synta圎rror - failed to compile a file into an Abstract Syntax Tree * F823: local variable name referenced before assignment These 5 are different from most other flake8 issues which are merely "style violations" - useful for readability but they do not effect runtime safety. _E901,E999,F821,F822,F823_ are the "_showstopper_" ( ) issues that can halt the runtime with a Synta圎rror, NameError, etc. mrcnn/model.py:2359:12: F632 use =/!= to compare str, bytes, and int literalsġ F632 use =/!= to compare str, bytes, and int literals Logging.warning("You are using the default load_mask(), maybe you need to define your own one.") mrcnn/utils.py:381:9: F821 undefined name 'logging' count -select=E9,F63,F72,F82 -show-source -statistics_ Import logging for line 382 ( ) testing of on Python 3.7.1
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