camlaedtke / cityscapes_segmentation_tf2

An end-to-end semantic segmentation pipeline on the cityscapes dataset

cityscapes_segmentation_tf2

In progress

An end-to-end semantic segmentation pipeline on the cityscapes dataset, using HRNetV2

Current experimentaion: how can we replicate SOTA results when using a single consumer GPU, rather than 8xV100s?

• Use mixed precision to cut down on VRAM usage
• In order to use larger batch sizes, we aggregate the graidents over multiple batches. This is done using a custom training loop defined in train_utils.py. Now we can use a batch size of 2, but with an "effective" batch size of 6 or 12. We can also subclass the train_step() function to accumilate gradients in order to use the fit() function rather than a custom training loop.
• But we still have to problem of batch statistics being computed over only a couple examples. To solve this, group normalization is used instead.