Open source Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in PyTorch, OpenCV (compiled for GPU), TensorFlow 2 for GPU, PyG and NVIDIA RAPIDS

GPU-accelerated Docker container with OpenCV 4.5, Python 3.8 , GStreamer and CUDA 10,2

Dockerfile for compiling OpenCV 4.6, Python 3.9, GStreamer with CUDA 11.7

Opencv, Opencv-contribt, Tensorflow-GPU, Keras, Zeromq, and DS-Tools docker

Docker file for Caffe SSD python3 with opencv and cuda

Dockerfile for recent tensorflow with GPU support and openCV

Ubuntu with the GPU and OpenCV support

tensorflow + opencv + cuda+ docker = gpu fun

Docker container with CUDA 10, CUDNN 7, OpenCV 4.3.0 and Tensorflow GPU

Darknet docker with the GPU and OpenCV support

Docker image for Deep Learning development with Tensorflow GPU, openCV and Jupyter

This project provides a Docker-based environment for running OpenCV with CUDA support, enabling GPU-accelerated computer vision tasks. The setup includes a complete build of OpenCV 4.12.0 with CUDA 12.8.1 and cuDNN support.

No description available

No description available

Darknet + OpenCV + Docker + GPU

create opencv/pcl develope environment

Docker image with GPU capacities with CUDA and CUDNN, along with a GPU ready OpenCV

Docker files for OpenCV+GPU

CUDA enabled image with Python 2, Theano, Keras & OpenCV

tensorflow_2.0_gpu_py3_jupyter_ffmpeg_opencv4 Docker镜像构建源码

YOLO pose estimation: C++ | ONNX Runtime | OpenCV | Docker | GPU

To create docker image support OpenCV GPU enable on Azure Container Instance

Real-time people counting system using YOLOv8 and OpenCV with threshold-based email alerts. Features a Streamlit dashboard, Dockerized deployment, and GPU-ready inference on DXA-100.

A real-time AI surveillance system for detecting objects, weapons, and zone intrusions, with instant alerts via email/SMS. Built with Flask, OpenCV, YOLOv8, DeepSORT, and Docker — deployed on AWS EC2 with GPU acceleration.