simurgailab / installation-guide-of-maskrcnn Goto Github PK

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.

git clone https://github.com/matterport/Mask_RCNN.git

🔺 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

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