In my case, I ran conda activate mask_rcnnĬonfirm that the environment is active by looking for “(mask_rcnn)” on the left side of the command promptĭownload and install CUDA Toolkit 10.1 Update 2 from the CUDA Toolkit Archive. Nothing special about the name mask_rcnn at this point, it’s just informative.įollow the instructions to activate the environment. This will create a new Python 3.7 environment called “mask_rcnn”. Run the following command conda create -n mask_rcnn python=3.7 Open the newly installed “Anaconda Prompt” ( Anaconda prompt documentation) Once Anaconda is installed, you will need to set up a new environment for ML-Agents. Run the installer ( Anaconda installation documentation)

It’s completely free and works on Windows, Mac, and LinuxĬhoose your operating system (e.g. While there are other ways to install Python, I find that Anaconda is the easiest way to manage multiple Python environments.

Rather than wait to see if/when the Matterport repo maintainers would take up the fix, I decided to copy his changes over to my own repo and add a few more of my own (yay MIT license!!). Turns out GitHub user had a pull request waiting with most of the changes implemented. Since the Complete Guide to Creating COCO Datasets course uses Mask R-CNN, I wanted to see if I could get a newer version to make setup easier. For that reason, installing it and getting it working can be a challenge.

It works on Windows, but as of June 2020, it hasn’t been updated to work with Tensorflow 2. Matterport’s Mask R-CNN is an amazing tool for instance segmentation.

Coco instal the new version for apple