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Inference-Tensorflow-Object-Detection-And-PyTorch-Classification

Table of Contents

About The Project

Code to automatically run object detection and classification using Tensorflow and PyTorch through a docker container

Requirements

  • Docker

Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

  • Install Docker

Installation

  1. Clone the repo
git clone https://github.com/tachillon/Inference-Tensorflow-Object-Detection-And-PyTorch-Classification

Usage

python3 build_docker_and_launch_inference.py --workdir <DIR> --imgdir <DIR>
build_docker_and_launch_inference.py/
inference_detection_classification.py/
Dockerfile/
├─ results/
├─ model/
│  ├─ frozen_inference_graph.pb
│  ├─ detection_label.pbtxt
│  ├─ resnext101_32x8d.pt
│  ├─ classification_labels.txt
├─ images/
│  ├─ img1.jpg
│  ├─ img2.jpg
│  ├─ img3.jpg

Caution: models to detect/classify objects are not provided.

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Achille-Tâm GUILCHARD - achilletamguilchard@gmail.com