v0.1.0 (superseded: package cannot complete its install)

Do not install this package. It installs and its launcher runs, but labeloxav install fails while building the container images with License file does not exist: LICENSE: the commit that added the licence pointed the package metadata at that file, and the Dockerfile did not copy it before its first dependency install. Found by running the published package end to end on a clean Ubuntu 24.04. A fixed release, v0.1.1, follows with the same contents and a test that prevents this recurring.

The notes below are the original notes for this release and still describe the product accurately.

---

The first tagged release, and the first Debian package.

What LabeloxAV is

An annotation and data engine for autonomous driving, built around Indian roads. It ingests dashcam
video and raw sensor drives, auto-labels behind a confidence gate, routes everything to human review,
tracks objects across frames and cameras, lifts boxes into 3D from LiDAR or estimated depth, measures
accuracy per class with confidence intervals, and exports training data in COCO, YOLO, KITTI and
OpenLABEL. 178 classes, with entries global datasets never had: autorickshaws, hand carts, cattle on the
carriageway.

What the package is, and is not

labeloxav_0.1.0_all.deb installs the source tree under /opt/labeloxav and a labeloxav command.
The product runs as Docker containers built from that tree, so Docker is a dependency and the package
does not turn LabeloxAV into native services.

sudo apt install ./labeloxav_0.1.0_all.deb
sudo labeloxav install        # builds images, starts everything, migrates, creates the admin
labeloxav url                 # http://localhost:3000

No GPU is required for annotation, review, search over existing data, governance and export. The
model paths that need CUDA refuse with a message rather than pretending. A clean install on a machine
with no GPU at all has not yet been tested; that sentence will be removed when it has.

The honest state

• 600,133 labels in the reference corpus, 788 with a human verdict. Most quality numbers are wide.
• The HD map runs end to end for the first time in this release, and two of its outputs are not yet
  trustworthy: fusion can emit a boundary far longer than the drive it came from, and paired lane
  widths run narrow. The documentation says so.
• Four backing services is not a light footprint. The clean-install footprint has not been measured.

Also in this release

• Apache License 2.0. Earlier snapshots were source-visible with no licence.
• A twenty minute narrated tour of every page, with every spoken number read from the live database
  at record time, linked from the README.
• The georeferencer now reads each camera's real calibration. It previously used a nominal lens that
  placed objects 2.5 times too far away near the car and 15 times too far at the horizon.
• Both HD map exports carry lane topology. Previously neither was routable.