Segmentation Made Simple: A Guide to KittiSeg

Welcome to the world of KittiSeg! This open-source project leverages an FCN (Fully Convolutional Network) based model specifically designed for segmenting roads. Its remarkable achievement of clinching the first place on the Kitti Road Detection Benchmark speaks volumes about its efficacy.

Getting Started with KittiSeg

This guide will walk you through the essential steps to install, set up, and effectively utilize KittiSeg for your segmentation tasks.

Requirements

• TensorFlow 1.0
• Python 2.7
• Necessary libraries: matplotlib, numpy, Pillow, scipy, commentjson

You can install the libraries using:

pip install numpy scipy pillow matplotlib commentjson

Alternatively, you can use:

pip install -r requirements.txt

Setup

1. Clone the repository:

git clone https://github.com/MarvinTeichmann/KittiSeg.git

2. Initialize all submodules:

git submodule update --init --recursive

3. Optional: Download Kitti Road Data:
   1. Retrieve Kitti data here
   2. Run the command:

      python download_data.py --kitti_url URL_YOU_RETRIEVED

Note that running the model using demo.py does not require you to download the Kitti data; this step is mandatory only if you wish to train your own model or evaluate it against the official scores.

Troubleshooting: Common Issues and Solutions

While using KittiSeg, you might encounter challenges. Here’s how to tackle some common ones:

• Model fails to start: Ensure all dependencies are installed correctly. Run the installation commands again to verify.
• Submodules not updating: Make sure to run the submodule update command after pulling the latest patches.
• Low performance: If the model runs slowly, check the resource allocation. Ensure you are using sufficient GPU resources if available.

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Understanding the Code: An Analogy

Think of KittiSeg as a master painter meticulously working to create a perfect piece of art. The process involves several stages:

• Enrollment (Setup): Just like a painter gathers their materials — brushes, canvases, and paints — you gather your datasets and install the required libraries.
• The Sketch (Model Training): The process begins with creating a rough outline of the painting. In KittiSeg, this is akin to initializing your model with basic segmentation tasks by training on the data.
• Layering Details (Fine-Tuning): The painter adds layers upon layers to create depth and richness in the artwork. Similarly, you refine the model’s performance through evaluations and adjustments to hyperparameters.
• The Gallery Opening (Deployment): Finally, when the painting is ready, it’s displayed for the world to appreciate. When you run your model on new data, you’re showing it off, doing segmentation in real-time at speeds of 95ms per image.

Manage Data Storage

KittiSeg allows separation of data storage from the code, enhancing organization, especially in server environments. By default, data is stored in KittiSegDATA and output in KittiSegRUNS. You can modify the storage paths by setting environment variables in your profile:

export TV_DIR_DATA=MYLARGEHDDDATA

Then all data will be downloaded to MYLARGEHDDDATA/data_road.

Experiment Organization with KittiSeg

KittiSeg simplifies managing multiple experiments by storing the output of each run in a unique directory. Each directory contains:

• output.log: A copy of the training output
• tensorflow events: For tensorboard visualization
• tensorflow checkpoints: To load the trained model
• Images: Visual output of predictions
• Model_files: All source code for reference

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.