Attention❗️
-
Compatible Python Version: python==3.6.12
-
IDE: Anaconda Cloud & Conda Prompt
-Anaconda Cloud: https://www.anaconda.com
🔺 Step 1: Compatible with Python 3.6 version, a virtual environment named maskrcnn is created in conda prompt.
conda create -n maskrcnn python=3.6.12
🔺 Step 2: The maskrcnn virtual environment is activated.
conda activate maskrcnn
🔺 Step 3: The Mask RCNN published by Matterport is cloned from the GitHub repository.
🔺 Step 4: Mask RCNN must be installed in the requirements.txt file located in the GitHub store. The requirements.txt file will load the libraries needed for your project in batch.
pip install -r requirements.txt
Dependencies
numpy, scipy, cython, h5py, Pillow, scikit-image, tensorflow==1.14.0 keras==2.0.8, jupyter or (tensorflow==1.15.0 keras==2.2.5)
For GPU: tensorflow-gpu:1.15.0, keras:2.2.5 For CPU: tensorflow:1.14.0, keras:2.0.8, h5py:2.10.0
🔺 Step 5: Download the pre-trained weights from https://github.com/matterport/Mask_RCNN/releases.
Download the file mask_rcnn_balloon.h5 from Mask_RCNN_2.1 file and mask_rcnn_coco.h5 model from Mask_RCNN_2.0 file. These 2 models should be placed in the samples folder.
Attention❗️
If the TensorFlow and Keras versions have landed in high versions, you can make a specific installation with the following commands.
🔺 Step 6: Running the setup.py file.
python setup.py install
🔺 Step 7: Loading the pycocotols module.
pip install git https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI
🔺 Step 8: Let's run it on the Jupyter notebook.
jupyter notebook
A view from the project: 