Updated Readme, Added Assets, Build Script

Author: animikhaich

CHANGELOG.md

@@ -7,9 +7,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## Version 0.1.0 Beta - 2021-04-06
 ### Added
+- Dockerfile
+- Launch Script (Dependency: [GNU Parallel](https://www.gnu.org/software/parallel/))
+- Automatic Tensorboard Initialization (Using the Launch Script) on Port 6006
 - **Frontend**:
   - Streamlit Dashboard For Easy Training and Visualization
-  - Epoch Count and Batch Progress Bar
+  - Epoch Count and Batch Progress Bar with Training Status Message
   - Live Training & Validation Loss & Accuracy Plots on the dashboard using Plot.ly Graphs
   - Training and Validation Data Directory
   - Model Backbone Selector
@@ -20,6 +23,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   - Input Image Shape Selector
   - Training Precision Selector
   - Training Button
+  - Status Update with Final Validation Accuracy and Balloons Animation on Completion
 - **Data Loader**:
   - Optimized Tf.Data implementation for maximum GPU usage
   - Automatically handle errors such as corrupted images

README.md

@@ -1,2 +1,197 @@
-# Zero-Code-TF-Classifier
-A Dashboard based Image Classifier that does not require any programming
+[![Contributors][contributors-shield]][contributors-url]
+[![Forks][forks-shield]][forks-url]
+[![Stargazers][stars-shield]][stars-url]
+[![Issues][issues-shield]][issues-url]
+[![MIT License][license-shield]][license-url]
+[![LinkedIn][linkedin-shield]][linkedin-url]
+
+<!-- PROJECT LOGO -->
+<br />
+<p align="center">
+    <img src="assets/deep-learning.png" alt="Logo" width="150" height="150">
+
+  <h2 align="center">Zero Code TF Image Classification Trainer</h2>
+
+  <p align="center">
+    Start Training a State of the Art Image Classifier within Minutes with Zero Coding Knowledge
+    <br />
+    <a href="http://hub.docker.com/">Docker Image</a>
+    ·
+    <a href="https://github.com/animikhaich/Zero-Code-TF-Classifier/issues/new">Report Bug</a>
+    ·
+    <a href="https://github.com/animikhaich/Zero-Code-TF-Classifier/issues/new">Request Feature</a>
+  </p>
+</p>
+<p align="center">
+  <img src="assets/capture-complete.png" alt="Demo GIF">
+</p>
+<!-- TABLE OF CONTENTS -->
+
+## Table of Contents
+
+- [Table of Contents](#table-of-contents)
+- [About The Project](#about-the-project)
+- [Features](#features)
+- [Hardware Requirements](#hardware-requirements)
+  - [Hardware used for Development and Testing](#hardware-used-for-development-and-testing)
+  - [Minimum Hardware Requirements](#minimum-hardware-requirements)
+- [Getting Started](#getting-started)
+  - [Prerequisites](#prerequisites)
+  - [Built With](#built-with)
+- [Setup and Usage](#setup-and-usage)
+- [Changelog](#changelog)
+- [Roadmap](#roadmap)
+- [Contributing](#contributing)
+- [License](#license)
+- [Contact](#contact)
+    - [Animikh Aich](#animikh-aich)
+
+<!-- ABOUT THE PROJECT -->
+
+## About The Project
+
+Don't know How to Write Complex Python Programs? Feeling Too Lazy to code a complete Deep Learning Training Pipeline Again? Need to Quickly Prototype a Image Classification Model?
+
+Okay, Let's get to the main part. This is a **Containerized Deep Learning-based Image Classifier Training Tool** that allows anybody with some basic understanding of Hyperparameter Tuning to start training a Image Classification Model.
+
+For the Developer/Contributor: The code is easy to maintain and work with. No Added Complexity. Anyone can download and build a Docker Image to get it up and running with the build script.
+
+## Features
+
+- **Zero Coding Required** - I have said this enough, I will repeat one last time: No need to touch any programming language, just a few clicks and start training!
+- **Easy to use UI Interface** - Built with Streamlit, it is a very user friendly, straight forward UI that anybody can use with ease. Just a few selects and a few sliders, and start training. Simple!
+- **Live and Interactive Plots** - Want to know how your training is progressing? Easy! Visualize and compare the results live, on your dashboard and watch the exponentially decaying loss curve build up from scratch!
+
+If you want to go in-depth with the Technical Details, then there are too many to list here. I would invite you to check out the [Changelog](CHANGELOG.md) where every feature is mentioned in details.  
+
+## Hardware Requirements
+
+We recommend an [Nvidia GPU](https://www.nvidia.com/en-gb/graphics-cards/) for Training, However, it can work with CPUs as well (Not Recommended)
+
+[Google Cloud TPUs](https://cloud.google.com/tpu) are Supported as per the code, however, the same has not been tested.
+
+### Hardware used for Development and Testing
+
+- **CPU:** AMD Ryzen 7 3700X - 8 Cores 16 Threads
+- **GPU:** Nvidia GeForce RTX 2080 Ti 11 GB
+- **RAM:** 32 GB DDR4 @ 3200 MHz
+- **Storage:** 1 TB NVMe SSD 
+- **OS:** Ubuntu 20.10
+
+The above is just used for development and by no means is necessary to run this application. The Minimum Hardware Requirements are given in the next section
+
+### Minimum Hardware Requirements
+
+- **CPU:** AMD/Intel 4 Core CPU (Intel Core i3 4th Gen or better)
+- **GPU:** Nvidia GeForce GTX 1650 4 GB (You can go lower, but I would not recommend it)
+- **RAM:** 8 GB (Recommended 16 GB)
+- **Storage:** Whatever is required for Dataset Storage + 10 GB for Docker Image
+- **OS:** Any Linux Distribution
+
+## Getting Started
+
+### Prerequisites
+
+- [Docker](https://www.docker.com/)
+- [Nvidia Docker](https://github.com/NVIDIA/nvidia-docker)
+- [Python 3](https://www.python.org/)
+- [Git](https://git-scm.com/)
+
+### Built With
+
+- [Streamlit](https://streamlit.io/)
+- [Tensorflow](https://www.tensorflow.org/)
+
+## Setup and Usage
+
+1. Install [Docker Engine](https://docs.docker.com/engine/install/)
+2. Install [Nvidia Docker Engine](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) (Required only for System with Nvidia GPU)
+3. Set up the Dataset Structure:
+
+```sh
+.
+├── Training
+│   ├── class_name_1
+│   │   └── *.jpg
+│   ├── class_name_2
+│   │   └── *.jpg
+│   ├── class_name_3
+│   │   └── *.jpg
+│   └── class_name_4
+│       └── *.jpg
+└── Validation
+    ├── class_name_1
+    │   └── *.jpg
+    ├── class_name_2
+    │   └── *.jpg
+    ├── class_name_3
+    │   └── *.jpg
+    └── class_name_4
+        └── *.jpg
+```
+
+4. There are two methods:
+   - Pull the Image from Docker Hub
+   - Clone the Repo and Build the Docker Image using the [build script](build.sh)
+5. Once the Docker Image is Built/Pulled, run the image using the following command:
+
+```sh
+docker run -it --runtime nvidia --net host -v /path/to/dataset:/data <image-name>
+```
+
+6. After training the trained weights can be found at: `/app/model/weights` Inside the Container
+7. After training the Tensorboard Logs can be found at: `/app/logs/tensorboard` Inside the Container
+8. You can use `docker cp <container-name/id>:<path-inside-container> <path-on-host-machine>` to get the weights and logs out. Further details can be found here: [Docker cp Docs](https://docs.docker.com/engine/reference/commandline/cp/)
+
+
+## Changelog
+
+See the [Changelog](CHANGELOG.md).
+
+
+## Roadmap
+
+See the [Open Issues](https://github.com/animikhaich/Zero-Code-TF-Classifier/issues?q=is%3Aopen) for a list of proposed features (and known issues).
+
+See the [Changelog](CHANGELOG.md) a lost of changes currently in development.
+
+
+## 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](LICENSE.md) for more information.
+
+
+## Contact
+
+#### Animikh Aich
+
+- Website: [Animikh Aich - Website](http://www.animikh.me/)
+- LinkedIn: [animikh-aich](https://www.linkedin.com/in/animikh-aich/)
+- Email: [[email protected]](mailto:[email protected])
+- Twitter: [@AichAnimikh](https://twitter.com/AichAnimikh)
+
+
+[contributors-shield]: https://img.shields.io/github/contributors/animikhaich/Zero-Code-TF-Classifier.svg?style=flat-square
+[contributors-url]: https://github.com/animikhaich/Zero-Code-TF-Classifier/graphs/contributors
+[forks-shield]: https://img.shields.io/github/forks/animikhaich/Zero-Code-TF-Classifier.svg?style=flat-square
+[forks-url]: https://github.com/animikhaich/Zero-Code-TF-Classifier/network/members
+[stars-shield]: https://img.shields.io/github/stars/animikhaich/Zero-Code-TF-Classifier.svg?style=flat-square
+[stars-url]: https://github.com/animikhaich/Zero-Code-TF-Classifier/stargazers
+[issues-shield]: https://img.shields.io/github/issues/animikhaich/Zero-Code-TF-Classifier.svg?style=flat-square
+[issues-url]: https://github.com/animikhaich/Zero-Code-TF-Classifier/issues
+[license-shield]: https://img.shields.io/github/license/animikhaich/Zero-Code-TF-Classifier.svg?style=flat-square
+[license-url]: https://github.com/animikhaich/Zero-Code-TF-Classifier/blob/master/LICENSE.md
+[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555
+[linkedin-url]: https://linkedin.com/in/animikh-aich/
+[product-screenshot]: assets/face-blur-demo.gif

assets/capture-complete.png

@@ -0,0 +1 @@
+docker build . -t zero-code-tf-classifier