OK, so I read the paper. And I came to the same conclusion as @brendan said the reading group did - we need to master the pieces before we can implement the paper.
So, I’m wondering if we’ve bitten off quite a bit more than we can chew in a day! I wouldn’t want to get to the end of the day (plus, perhaps, the weekend) and find that we hadn’t achieved anything we were excited about. So I can think of two approaches:
- Start with something simpler at the start of the day, and make sure we can at least finish that. Specifically, I’m thinking The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
- Or, start with one sub-piece of the full mask R-CNN, such as the region proposal network (RPN)
There’s already a faster R-CNN implementation (and therefore RPN) at https://github.com/yhenon/keras-frcnn and one for pytorch at https://github.com/longcw/faster_rcnn_pytorch . So I’m less excited about this.
OTOH, I’m really excited about doing the tiramisu! Here’s my pitch:
- There are already 2 densenet pytorch implementations, and they both look pretty good, but there’s no tiramisu implementation
- I’ve been hoping to teach the use of skip connections in segmentation in this part of the course, so this would give me just what I need
- As well as teaching tiramisu, this would be a great excuse to teach densenet too!
- The results from densenet and tiramisu are both state of the art, yet are easy to understand
- @yad.faeq has already started an (incomplete) keras port, so maybe he can help us too…
If we get it done, we could then move on to parts of mask r-cnn later in the day or over the weekend, if there’s interest.
What do you all think?