iVIT-T

A library to training model of TensorFlow-Keras and Darknet. This library enables the training of the model of classification, object detection.

• See What's New
• Hardware Recommendations
• Software Requirements
• Build convert docker images and database container
• Web API mode
• Web UI
• The format of dataset
• Reference

See What's New

• Release Notes

Getting Started

Hardware Recommendations

NOTE : In case of the use of another hardware, the correct functionality can not be guaranteed.

Item	Information
CPU	Intel® 12th Gen Core™i7/i5 processors.
GPU	NVIDIA RTX A2000, A4500
RAM	32GB
Storage	1T
OS	Ubuntu 20.04.4

Software Requirements

Install nvidia-driver(510+), nvidia-docker and docker before installing the docker container.

• Tutorial-nvidia-driver

• Tutorial-docker

• Tutorial-nvidia-docker

• Add docker to sudo group

  • Tutourial

  sudo groupadd docker
  sudo usermod -aG docker $USER
  sudo chmod 777 /var/run/docker.sock
  

Run container

Build convert docker images and database container

sudo chmod 777 ./docker
sudo ./docker/init_env.sh -p 0,1,2,3

In the "init_env.sh", this "-p" is the model finally deployed platform:

0: Nvidia, 1: Intel, 2: Xilinx, 3: Hailo

Web API mode

sudo ./docker/run.sh -p 6530

In the "run.sh", this "-p" is the port number, you can setting haven't used the port number.

• Tutorial

Web UI

If you want to use the UI version, you can follow this Tutorial:

• Tutorial

The format of dataset

• Image format: .jpg/.jpeg/.png/.bmp/.JPG/.JPEG/.PNG/.BMP

Classification

• Folder(class)/img1, img2, ..., imgN

├── class_1
│   ├── 1.jpg
│   ├── 2.jpg
│   ├── 3.jpg
...
│   ├── 29.jpg
│   └── 30.jpg
└── class_2
    ├── 1.jpg
    ├── 2.jpg
    ├── 3.jpg
    ...
    ├── 29.jpg
    └── 30.jpg

Object detection

• Folder/img1, txt1, img2, txt2, ..., imgN, txtN

Folder
├── 0.jpg
├── 0.txt
├── 10000.jpg
├── 10000.txt
├── 10001.jpg
├── 1000.jpg
├── 1000.txt
├── 10069.jpg
├── 10069.txt
├── 1006.jpg
├── 1006.txt
├── 10078.jpg
...
├── 840.jpg
└── 840.txt

• Annotation format: .txt (YOLO)

    Format:
        index x y w h
    Example:
        0 0.4014 0.3797 0.0801 0.0859

• Mapping class filename: classes.txt

    label1
    label2
    ...

Reference

• Darknet
  • https://github.com/AlexeyAB/darknet
• Tensorflow
  • https://github.com/tensorflow/tensorflow
• OpenVINO:
  • https://docs.openvino.ai/latest/home.html
  • https://github.com/openvinotoolkit/openvino
• Vitis-AI:
  • https://github.com/Xilinx/Vitis-AI.git
  • https://github.com/Xilinx/Vitis-AI
• Hailo:
  • https://hailo.ai/developer-zone/documentation/sw-suite-2023-01-1/?sp_referrer=suite_install.html
  • https://github.com/hailo-ai/hailort
• To convert the model to someone format, we refer to the repository:
  • https://github.com/david8862/keras-YOLOv3-model-set
  • https://github.com/Tianxiaomo/pytorch-YOLOv4.git
• Flask
  • https://github.com/pallets/flask
• Sample images from Pexels
  • https://www.pexels.com/
• Sample images from roboflow
  • https://universe.roboflow.com/
• Segmentation models
  • https://github.com/qubvel/segmentation_models

  @misc{Yakubovskiy:2019,
      Author = {Pavel Iakubovskii},
      Title = {Segmentation Models},
      Year = {2019},
      Publisher = {GitHub},
      Journal = {GitHub repository},
      Howpublished = {\url{https://github.com/qubvel/segmentation_models}}
      }