-
Notifications
You must be signed in to change notification settings - Fork 384
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Implement Feature Pyramid Network for semantic segmentation #60
Comments
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Splitting off of #12. Eventually we want to implement an object detection architecture in addition to out current semantic segmentation architecture. The RetinaNet (ticketed in #12) is a perfect fit for our goals. It will be based on top of a ResNet feature extractor and a feature pyramid network.
We can use the ResNet feature extractor and the feature pyramid network already for semantic segmentation. Then later down the line we can extend it adding a bounding box regression head and get a single unified simple architecture for both object detection as well as semantic segmentation.
Resources:
Here is the main gist from the second PDF:
The ResNet will give us the leftmost upward path. The downward path and the lateral connections then make up the feature pyramid network. The rightmost part is the semantic segmentation head.
Tasks
The text was updated successfully, but these errors were encountered: