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Kittibox

Kittibox is a Python and TensorFlow repository of scripts for using and training FastBox, a car-detection model, on the KITTI Object Detection Dataset. It includes demo, training, evaluation, and visualization workflows.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Kittibox — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Kittibox when you need a codebase for experimenting with FastBox car detection, especially when working with KITTI data or examining the model's training and evaluation workflow.

Who it is for

  • Computer-vision researchers exploring car detection on KITTI data
  • Developers who want to inspect or adapt the FastBox training configuration
  • Practitioners comparing object-detection inference speed on their own setup

Documented uses

  • Generate a car-detection prediction for an input image
  • Train FastBox using KITTI object-detection data
  • Evaluate or visualize model results
  • Adapt configuration modules for another dataset or encoder
Before you choose

Requirements & limits

The repository is public and specifies an MIT license.

  • The project requires TensorFlow 1.0 or later and lists matplotlib, NumPy, Pillow, SciPy, and runcython.
  • Training and official-score benchmarking require KITTI data; building the evaluation code is optional and specific to benchmarking.
  • The README presents Linux as the development and maintenance environment and says Windows use requires modifications.
  • FastBox's reported speed and accuracy figures are measurements on KITTI data in the README, not a guarantee for other hardware or datasets.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up Kittibox and run the sample-image demo

Prepare the repository and request a prediction for the included demo image.

Before you start

  • Python environment with TensorFlow 1.0 or later
  • matplotlib, NumPy, Pillow, SciPy, and runcython
  • Linux is the project's stated development and maintenance environment

Steps

  1. Clone the Kittibox repository.

  2. Initialize its submodules with the command below.

    Documented command
    git submodule update --init --recursive

  3. Build the Cython utilities with cd submodules/utils && make.

  4. Run the command below.

    Documented command
    python demo.py --input_image data/demo.png

Expected outcome

The demo command requests a prediction using the included sample image.

Guide limitations

  • The README says the demo does not require downloading KITTI data or building the optional KITTI evaluation code.
  • Windows use may require modifications.
Documentation 1 ↗
Source-based practical guide

Prepare KITTI data for FastBox training

Set up the repository and KITTI data needed to train a model.

Before you start

  • Python environment with TensorFlow 1.0 or later
  • matplotlib, NumPy, Pillow, SciPy, and runcython
  • KITTI Object Detection Dataset download URL

Steps

  1. Clone the Kittibox repository.

  2. Initialize all submodules and build the Cython utilities.

  3. Retrieve the KITTI data URL and run the command below to download, extract, and prepare the data.

    Documented command
    python download_data.py --kitti_url URL_YOU_RETRIEVED

  4. Run the command below to train on the KITTI data.

    Documented command
    python train.py

Expected outcome

The documented workflow prepares KITTI data and starts FastBox training.

Guide limitations

  • The README identifies Linux as the development and maintenance environment.
  • Building the KITTI evaluation code is an additional step only for benchmarking with evaluate.py.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
MarvinTeichmann/KittiBox · Official homepage title · retained in its source language
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