---
license: apache-2.0
datasets:
- HyzeAI/HyzeACR-Dataset
language:
- en
pipeline_tag: image-classification
tags:
- HyzeAI
- HyzeACR
- HiteshV
- MadeByKids
- OpenSource
- Space
- Astronomy
---

<p align="center">
  <img src="https://i.ibb.co/99ybtt8Y/Hyze-ACR-Banner.png" alt="HyzeACR" width="650"/>
</p>

<h1 align="center">HyzeACR</h1>

<p align="center">
  A lightweight image-classification model by <b>HyzeAI</b>
</p>

<p align="center">
   <a href="https://hyze.dev">Chat with all models</a> •  
   <a href="https://hyzeacr.netlify.app">HyzeACR (Web Demo)</a>
</p>

---

## The Live Demo
Go to [https://hyzeacr.netlify.app](https://hyzeacr.netlify.app)
---

## What This Project Does

This AI system classifies space related images into the following categories:

-  Moons  
-  Planets  
-  Galaxies  
-  Nebulae

It supports:
- Image based classification
---

## How It Works

1. A trained machine learning model is loaded in the browser using TensorFlow.js
2. Any image of a Moon, Planet, Nebulae, or a Galaxy is uploaded to the model
3. The model predicts the most likely space object  
4. The predictions are displayed

---

## How to use

1. The easiest way to use the model is by using the web demo at [https://hyzeacr.netlify.app](https://hyzeacr.netlify.app) (The model is hosted with Google Cloud)
2. Install a local TensorFlow/Keras environment
3. Run this command pip install tensorflow numpy
4. Next write a python script to run the model

---

## Tech Stack

- TensorFlow.js (browser inference)
- Keras (model training)
- TensorFlow (ML framework)
- JavaScript (frontend logic)
- HTML (UI)

---

## Created by Hitesh Vinothkumar