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README.md

Multi-Task Training Example

In this example, we are going to train Multi-Task Light-Weight RefineNet for joint semantic segmentation and depth estimation. Note that inference examples together with pre-trained weights can be found in the official repository.

The hyperparameters set here are not the same as used in the corresponding paper, hence the results will differ. Please refer to the paper below for more information on the model and the training regime.

Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations
Vladimir Nekrasov, Thanuja Dharmasiri, Andrew Spek, Tom Drummond, Chunhua Shen, Ian Reid
In ICRA 2019

Prepare Data

Considering that you successfully installed the DenseTorch package, the next step is to download the NYUDv2 dataset with segmentation and depth masks. The dataset can be downloaded by following the link.

After downloading and unpacking the archive, create the datasets folder and link the nyudv2 directory in the archive to the just created folder:

mkdir datasets
ln -s /path/to/nyudv2 ./datasets/

Training

Now you are ready to run the example script. To do so, simply execute python train.py. After it is finished, the best model will be stored in the corresponding pth.tar file. Note that it would the model that improves upon the previous checkpoint both in terms of mean IoU (for segmentation) and linear RMSE (for depth estimation).

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