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This is the code from a small project I worked on during the summer of 2017. It explored the generation of image semantic segmentation training data from 3D renders. You can read about it here.

Training the Model

  1. Download the NYU V2 labelled dataset and save it somewhere.
  2. cd src and pip3 install -r requirements.txt
  3. cd .. and run python3 src/ <PATH TO NYU DATASET>. This will populate model/nyu_data with the baseline data in the correct format.
  4. Install model/caffe-segnet-cudnn5 by following the instructions that are somewhere online :)
  5. Create a symlink from /SegNet to the model/ folder. Alternatively, you can go through all files and replace instances of /SegNet with the path to the model folder. Sorry, I know this isn't ideal, but it's how caffe-segnet wanted things to be installed.
  6. Run /SegNet/caffe-segnet-cudnn5/build/tools/caffe train -gpu <GPU_ID> -solver <SOLVER_PROTOTXT>, where SOLVER_PROTOTXT is either /SegNet/models/nyu_segnet_solver.prototxt or /SegNet/models/combined_segnet_solver.prototxt based on whether or not you want to exclude the synthetic images I've generated already.
  7. Wait for a long time depending on how good your gpu is. You may need to fiddle with batch size to get it to run more effectively - look at the caffe-segnet tutorial for help here.
  8. Run python3 src/ to generate your final .caffemodel file. I think that you can find info on this in the caffe-segnet tutorial.
  9. Run python3 src/ on the images you want to test your model with.

Generating Synthetic Examples

If you just want to see some synthetic examples I've already generated, look in model/syn_data/images. Otherwise, you can follow these steps to make your own.

  1. Fire up Blender and model and light a scene of your choosing (cycles rendering engine only).
  2. Change your Blender scripts directory to point to blender_scripts/
  3. Select relevant objects and use the Label Selected (syntrain) addon to apply a label to them.
  4. Open src/ in your script editor. This will set up the compositor nodes to output the labels and images from your scene to a folder named render/ in the same directory as your .blend file. If you make changes to labels after running this script, you need to delete these created compositor nodes and run it again. Again, not ideal, but it doesn't take that long.
  5. Render your scene and check the render/ folder for output.
  6. Note that if you plan on using these images to train this model, you'll need to update the relevant train.txt file and recalculate the class weights using python3 src/

Blender Scene Credits


Generation of image semantic segmentation training data from 3D renders.






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