GluonCV Backend Next Steps

If someone wishes to work on a more robust implementation of this GluonCV backend for Raster Vision, below are some ideas:

- Support more GluonCV models. Currently only ResNet50v2 is supported.
- Benchmarking with existing Keras Classification backend.
- Support more tasks. Currently, only chip classification is implemented; however, GluonCV also supports object detection, semantic segmentation, and instance segmentation.
- Use full Spacenet Vegas dataset. Currently, the largest subset used for training was about one third of the full dataset.
- Use new dataset. Recommendation: [COWC Potsdam](https://gdo152.llnl.gov/cowc/)
- Improve batch size. Currently, something about CUDA's configuration does not work with a batch size of greater than 32.

This issue is a roadmap of several independent GluonCV backend ideas rather than one defined change. Start by choosing a single direction, then inspect the existing backend implementation and its current ResNet50v2 chip-classification behavior. Done should mean the selected capability is implemented and demonstrated with the relevant model, task, dataset, benchmark, or batch-size result.

Written by the indexing model from the issue text.