- ResNet 18 - Image Classification - ImageNet (not available for tensorflow)
- ResNet 50 - Image Classification - ImageNet
- MobileNet v2 - Image Classification
- Why
- Small architecture, and available on all platforms (including tensorflow)
- Common vanilla model for edge devices (smart phones, etc)
- Why Not
- Not too different from ResNet 18
- Why
- VGG 16 - Image Classification
- Why
- Classic deep learning archiecture
- Available in all model zoos
- Fewer layers than ResNet 18 or 50, but significant more parameters
- Why Not
- Not dramatically different from classic image processing
- Doesn't expose us to more varied architectures
- Why
- DenseNet 121 - Image Classification
- Why
- Modern deep learning architecture
- Smaller footprint than ResNet 50
- Available in all model zoos
- Why Not
- Not dramatically different from classic image processing
- Why
- BERT - Machine Comprehension
- Why
- Interesting new architecture, lots of customer asks
- Different architecture, uses attention
- GPU utilization is wholy different from CNNs
- Why Not
- Not available in the vanilla model zoos in pytorch/tensorflow
- May be somewhat tricky to make an apples/apples comparison as architecture requires custom layers
- Why
- Faster RCNN - Image Object Detection
- Why
- Common image problem type
- Common model architecture
- Available on all model zoo's
- Why Not
- Not dramatically different from image classification in terms of compute flow
- Why