^{Semantic Segmentation}</s></p>

FCN-Alexnet Patches for TensorRT

There exist a couple non-essential layers included in the original FCN-Alexnet which aren't supported in TensorRT and should be deleted from the deploy.prototxt included in the snapshot.

At the end of deploy.prototxt, delete the deconv and crop layers:

layer {
  name: "upscore"
  type: "Deconvolution"
  bottom: "score_fr"
  top: "upscore"
  param {
    lr_mult: 0.0
  }
  convolution_param {
    num_output: 21
    bias_term: false
    kernel_size: 63
    group: 21
    stride: 32
    weight_filler {
      type: "bilinear"
    }
  }
}
layer {
  name: "score"
  type: "Crop"
  bottom: "upscore"
  bottom: "data"
  top: "score"
  crop_param {
    axis: 2
    offset: 18
  }
}

And on line 24 of deploy.prototxt, change pad: 100 to pad: 0.

Finally, copy the fpv-labels.txt and fpv-deploy-colors.txt from the aerial dataset to your model snapshot folder on Jetson. Your FCN-Alexnet model snapshot is now compatible with TensorRT. Now we can run it on Jetson and perform inference on images.

Back | <b><a href="segnet-training.md">Training FCN-Alexnet with DIGITS</a></p> </b><p align="center">^{© 2016-2019 NVIDIA |}<a href="../README.md#two-days-to-a-demo-digits">^{Table of Contents}</a></p>