michaelnguyen11 / edgeai-yolox

YOLOX-ti-lite models are optimized for deployment on TI edge processors

YOLOX based Models

This repository is a fork of YOLOX. This contains the enhancements of the YOLOX repository for supporting additional tasks and embedded friendly ti_lite models.

Installation

Step1. Install YOLOX.

./setup.sh

Step2. Install pycocotools.

pip3 install cython; pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'

Tasks supported

• 2D Detection (with Ti-lite models)

  • These YOLOX based 2D detection models are optimized for TI processors.
  • Refer to this readme for further details.

• 6D Pose Estimation

  • 6D pose estimation or object pose estimation aims to estimate the 3D orientation and 3D translation of objects in a given environment. In this work, we propose a multi-object 6D pose estimation framework by enhancing the YOLOX object detector. The network is end-to-end trainable and detects each object along with its pose from a single RGB image without any additional post-processing.
  • Refer to this readme for further details.

• Keypoint Detection / Human Pose Estimation

  • Multi person 2D pose estimation is the task of understanding humans in an image. Given an input image, target is to detect each person and localize their body joints. In this work, we introduce a novel heatmap-free approach for joint detection, and 2D multi-person pose estimation in an image based on the popular YOLO object detection framework.
  • In general, this can be called Keypoint Detection or 2D Pose Estimation.
  • Refer to readme for further details.

Sample Inferences

• Given below are sample inferences for the tasks of human pose estimation and 6d pose estimation.

  Human Pose Estimation	6D Pose Estimation

Note:

See the original documentation