Dockerized workspace for 2D/3D object detection

Anything related to Robotics and Autonomous Vehicle Perception using Deep Learning.
May include classification, detection, segmentation, tracking, pose estimation.

Framework: Both Tensorflow and Pytorch supported.

Libs: Fastai, tensorflow models, second.pytorch and detectron2 also included

Data: Most experiments would be done on public datasets like Kitti, and sometimes smaller data like Pets for quick iteration.

Environment variables for easy access to different paths:

• TF_OBJECT_DETECTION_API
• SECOND_API

Included Notebooks/Scripts

• Tensorflow Object Detection
  • Pets
  • Kitti
• Pytorch
  • Senond/Pointpillars
• Fast.ai examples from course

Docker

Setup

Recommended symlink your data dir to ./data

$ ln -s <path_to_your_data_root> data

Build

$ make docker-build

Run

Here are few options, use as needed:

• Default entry $ make docker-run-default
• Bash into an existing container $ make docker-exec-bash
• Run with bash only $ make docker-run-bash
• Jupyter Lab in docker $ make docker-run-jupyter

Makefile variables

You can override the default values of different components of the docker build or run options using makefile variables from the command line. See config.mk for details, here are some examples:

• DATA: path to data
• TF_PACKAGE_VERSION: Tensorflow version
• TORCH_VERSION: Pytorch version

Usage:

$ make docker-build TF_PACKAGE_VERSION=2.0.0