efficientnet(scalings, block_config;
             inchannels = 3, nclasses = 1000, max_width = 1280)

Create an EfficientNet model (reference).

Arguments

• scalings: global width and depth scaling (given as a tuple)
• block_config: configuration for each inverted residual block, given as a vector of tuples with elements:
  • n: number of block repetitions (will be scaled by global depth scaling)
  • k: kernel size
  • s: kernel stride
  • e: expansion ratio
  • i: block input channels (will be scaled by global width scaling)
  • o: block output channels (will be scaled by global width scaling)
• inchannels: number of input channels
• nclasses: number of output classes
• max_width: maximum number of output channels before the fully connected classification blocks