Chris DeBellis, a lead AI data scientist at Honeywell, helps us understand what Mask R-CNN is and why it’s useful for robot perception. We also explore how this method compares with other convolutional neural network approaches and how you can get started with Mask R-CNN.
Changelog++ members support our work, get closer to the metal, and make the ads disappear. Join today!
Sponsors:
- DigitalOcean – Enjoy CPU optimized droplets with dedicated hyper-threads from best in class Intel CPUs for all your machine learning and batch processing needs. Easily spin up a one-click Machine Learning and AI application image and get immediate access to Python3, R, Jupyter Notebook, TensorFlow, SciKit, and PyTorch. Our listeners get $100 in credit!
- Hired – Salary and benefits upfront? Yes please. Our listeners get a double hiring bonus of $600! Or, refer a friend and get a check for $1,337 when they accept a job. On Hired companies send you offers with salary, benefits, and even equity upfront. You are in full control of the process. Learn more at hired.com/practicalai.
Featuring:
- Chris DeBellis – Website
- Chris Benson – Website, GitHub, LinkedIn, X
- Daniel Whitenack – Website, GitHub, X
Show Notes:
- Matterport R-CNN
- Mask R-CNN paper
- COCO dataset
- Stanford CNN course
- Stanford Deep Learning course
- Facebook’s Detectron
Something missing or broken? PRs welcome!