If anyone is interested, here’s a little project to test out your skills implementing and testing an architecture. What I’m looking for is pytorch implementations suitable for imagenet of preact resnet and the classifier backbone part of yolo v3 (which the paper calls darknet-53).
Note that there are some pytorch implementations of preact resnet out there already, but:
- They’re incomplete implementations of the paper (they don’t have the proper starting/ending blocks, for instance)
- They’re for cifar-10, not imagenet (I haven’t looked into it, but there seem to be some little differences).
In particular, I’m interesting in preact-resnet50 - although an implementation presumably would easily allow any of the resnets to be built.
There’s also an implementation of darknet-53 already for pytorch, but it is messy and ugly and uses a config file rather than a normal pytorch approach to defining the network.
I’m looking for implementations that are:
- Concise and readable (e.g refactor repeated code into modules)
- Tested against the paper to confirm you get the same results (I can help with this if you don’t have access to a suitable machine; but you can do some initial testing by at least running a hundred batches or so and comparing to the reference implementations in darknet / lua. You could also simply compare to regular rn50 for a hundred batches or so and confirm it’s faster.)
For bonus points, do an senet version of preact resnet
Let us know here if you start on this so that we can coordinate.