Lightning Kitti

Semantic Segmentation with Pytorch-Lightning


This is a simple demo for performing semantic segmentation on the Kitti dataset using Pytorch-Lightning and optimizing the neural network by monitoring and comparing runs with Weights & Biases.

Pytorch-Ligthning includes a logger for W&B that can be called simply with:from pytorch_lightning.loggers import WandbLoggerfrom pytorch_lightning import Trainer wandb_logger = WandbLogger() trainer = Trainer(logger=wandb_logger)

Refer to the documentation for more details.

Hyper-parameters can be defined manually and every run is automatically logged onto Weights & Biases for easier analysis/interpretation of results and how to optimize the architecture.

You can also run sweeps to optimize automatically hyper-parameters.

Note: this example has been adapted from Pytorch-Lightning examples.



  • A quick way to run the training scrip is to go to the notebook/tutorial.ipynb and play with it.


  1. Clone this repository.
  2. Download Kitti dataset
  3. The dataset will be downloaded in the form of a zip file namely Unzip the dataset inside the lightning-kitti/data_semantic/ folder.
  4. Install dependencies through requirements.txt, Pipfile or manually (Pytorch, Pytorch-Lightning & Wandb)
  5. Log in or sign up for an account -> wandb login
  6. Run python and add any optional args

Visualize and compare your runs through generated link

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Sweeps for hyper-parameter tuning

W&B Sweeps can be defined in multiple ways:

  • with a YAML file - best for distributed sweeps and runs from command line
  • with a Python object - best for notebooks

In this project we use a YAML file. You can refer to W&B documentation for more Pytorch-Lightning examples.

  1. Run wandb sweep sweep.yaml
  2. Run wandb agent <sweep_id> where <sweep_id> is given by previous command

Visualize and compare the sweep runs

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After running the script a few times, you will be able to compare quickly a large combination of hyperparameters.

Feel free to modify the script and define your own hyperparameters.

See the live report →


GitHub - borisdayma/lightning-kitti: Semantic Segmentation with Pytorch-Lightning
Semantic Segmentation with Pytorch-Lightning. Contribute to borisdayma/lightning-kitti development by creating an account on GitHub.