Datasetpublic

HyzeAI/HyzeACR-Polaris-Dataset

HyzeACR (Astronomical Character Recognition) An Image Classification model that detects galaxies, moons, planets, and nebulaes using TensorFlow and Keras The Live Demo Go to https://hyzeacr.netlify.app HyzeACR Polaris HyzeACR Polaris is an AI-powered astronomy image classification model designed to identify and categorize space objects from images with high accuracy. Built as part of the Hyze ecosystem, Polaris focuses on real-world… See the full description on the dataset page: https://huggingface.co/datasets/HyzeAI/HyzeACR-Polaris-Dataset.

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face

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HyzeACR (Astronomical Character Recognition)

An Image Classification model that detects galaxies, moons, planets, and nebulaes using TensorFlow and Keras

The Live Demo

Go to https://hyzeacr.netlify.app ---

HyzeACR Polaris

HyzeACR Polaris is an AI-powered astronomy image classification model designed to identify and categorize space objects from images with high accuracy.

Built as part of the Hyze ecosystem, Polaris focuses on real-world astronomical data and supports a wide range of celestial object classes.

Features

—Classifies multiple types of space objects
—Deep learning–based image recognition
—Designed for astronomy research and exploration
—Fast and efficient inference
—Built for integration with Hyze AI systems

Supported Classes

HyzeACR Polaris can detect and classify the following categories:

—Moon
—Nebula
—Black Hole / Quasar
—Lensed Quasar
—Earth
—Exoplanet
—Solar System Planets
—Unknown
—Star Clusters
—Galaxy
—Galaxy Cluster

Use Cases

—Astronomy research and education
—Space image labeling and organization
—AI-powered space exploration tools
—Integration into astronomy apps and platforms
—Dataset preprocessing and classification

Usage

Python (Keras)

python

from keras.models import load_model
from PIL import Image, ImageOps
import numpy as np

np.set_printoptions(suppress=True)

model = load_model("keras_Model.h5", compile=False)
class_names = open("labels.txt", "r").readlines()

data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)

image = Image.open("<IMAGE_PATH>").convert("RGB")
size = (224, 224)
image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)

image_array = np.asarray(image)
normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1

data[0] = normalized_image_array

prediction = model.predict(data)
index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = prediction[0][index]

print("Class:", class_name[2:], end="")
print("Confidence Score:", confidence_score)

Python (OpenCV + Keras)

python

from keras.models import load_model
import cv2
import numpy as np

np.set_printoptions(suppress=True)

model = load_model("keras_Model.h5", compile=False)
class_names = open("labels.txt", "r").readlines()

camera = cv2.VideoCapture(0)

while True:
    ret, image = camera.read()
    image = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA)

    cv2.imshow("Webcam Image", image)

    image = np.asarray(image, dtype=np.float32).reshape(1, 224, 224, 3)
    image = (image / 127.5) - 1

    prediction = model.predict(image)
    index = np.argmax(prediction)
    class_name = class_names[index]
    confidence_score = prediction[0][index]

    print("Class:", class_name[2:], end="")
    print("Confidence Score:", str(np.round(confidence_score * 100))[:-2], "%")

    if cv2.waitKey(1) == 27:
        break

camera.release()
cv2.destroyAllWindows()

Google Coral (Edge TPU)

Install dependencies:

bash

python3 -m pip install --extra-index-url https://google-coral.github.io/py-repo/ pycoral~=2.0 Pillow opencv-python opencv-contrib-python

Run inference:

python

import cv2
from pycoral.utils.dataset import read_label_file
from pycoral.utils.edgetpu import make_interpreter
from pycoral.adapters import common, classify

modelPath = '<PATH_TO_MODEL>'
labelPath = '<PATH_TO_LABELS>'

def classifyImage(interpreter, image):
    size = common.input_size(interpreter)
    common.set_input(interpreter, cv2.resize(image, size, interpolation=cv2.INTER_CUBIC))
    interpreter.invoke()
    return classify.get_classes(interpreter)

def main():
    interpreter = make_interpreter(modelPath)
    interpreter.allocate_tensors()
    labels = read_label_file(labelPath)

    cap = cv2.VideoCapture(0)
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break

        frame = cv2.flip(frame, 1)
        results = classifyImage(interpreter, frame)

        cv2.imshow('frame', frame)
        print(f'Label: {labels[results[0].id]}, Score: {results[0].score}')

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()

if __name__ == '__main__':
    main()

Contributing

Contributions are welcome! Feel free to open issues or submit pull requests to improve the model, dataset, or performance.

License

This project is licensed under the MIT License.

Part of Hyze

HyzeACR Polaris is part of the Hyze AI ecosystem, focused on building powerful, accessible AI tools across multiple domains.

Support

If you like this project, consider starring the repo and sharing it!