File size: 1,650 Bytes
747033e 8fcf7fa 238cbbd 6922c4b 238cbbd 6922c4b c96b4d7 6922c4b 8fcf7fa c96b4d7 6922c4b 8fcf7fa 6922c4b 747033e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | ---
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 |