Cosmic-Morphology

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Cosmic-Morphology

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Computer Vision project to recognize cosmic data (asteroid, comet, galaxy, nebula, planet, star) using NASA API to get images, ResNet50 as base model & PyTorch.

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# **Cosmic Morphology**

<p align="center">
  <img src="https://science.nasa.gov/wp-content/uploads/2023/04/potw2109a-jpg.webp" width="50%" alt="Hubble Beholds a Big, Beautiful Blue Galaxy">
  <br>
  <small><em>Hubble Beholds a Big, Beautiful Blue Galaxy<br>NGC 2336, captured by the NASA/ESA Hubble Space Telescope.</em></small>
</p>

## **Overview**
This **project** focuses on building a scalable data infrastructure to store and analyze real cosmic data retrieved from the NASA API. The primary goal is to develop a robust **multiclass classification model** capable of identifying various celestial phenomena, such as galaxies, nebulae, and stars.

## Objectives
- **Scalable Infraestructure:** Create a system for retrieving, storing and processing image data from APIs.
- **Classify Cosmic Morphology:** Multiclass classification model to identify 6 classes; asteroid, comet, galaxy, nebula, planet, star.
- **Enhance System Performance:** Further model fine-tuning, 
  
## **📂 Methods**

- **`1-Data-Collection`**: Scripts for data extraction from NASA API, fetching cosmic images.
- **`2-EDA`**: Notebooks and scripts for Exploratory Data Analysis (EDA), including class imbalance visualizations.
- **`3-Train-Val-Test-Split`**: Code for splitting data into training, validation, and test sets for model training.
- **`4-Machine-Learning`**: Contains:
  - **Baseline Model**: Initial ResNet50-based training and evaluation.
  - **Fine-Tuning**: Transfer learning with additional layers and parameters.
  - **Evaluation**: Metrics visualizations and model performance analysis.

## **Results**
<div align="center">
    <img src="4-Machine-Learning/metrics/fine_tuned_model_evaluation.png" alt="Fine-Tuned Model Evaluation" width="600"/>
</div>

## **Conclusions**

- The fine-tuned model showed modest improvements in precision and recall for certain classes, notably "nebula" and "galaxy".
  - Class imbalances and noisy data remain significant challenges affecting overall performance.

**Next Steps**
   - Clean and refine image data to remove mislabeled or irrelevant samples.
  - Try out other transfer learning models and pre-trained weights.
  - Expand the dataset by incorporating the [SpaceNet dataset](https://www.kaggle.com/datasets/razaimam45/spacenet-an-optimally-distributed-astronomy-data) and evaluate the impact of synthetic data augmentation on model generalization.

## **Bibliography**
- Lintott, C. J. et al. (2008). Galaxy Zoo: Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey. *Monthly Notices of the Royal Astronomical Society*, 389(3), 1179–1189.
- Willett, K. W. et al. (2013). Galaxy Zoo 2: Detailed morphological classifications for 304,122 galaxies from the Sloan Digital Sky Survey. *Monthly Notices of the Royal Astronomical Society*, 435(3), 2835–2860.
- Chollet, F. (n.d.). Image Classification from Scratch. *Keras*. Retrieved from [https://keras.io/examples/vision/image_classification_from_scratch/](https://keras.io/examples/vision/image_classification_from_scratch/)
- Chollet, F. (n.d.). Keras Metrics. *Keras*. Retrieved from [https://keras.io/api/metrics/](https://keras.io/api/metrics/)
- Nicholas Renotte. (n.d.). Build a Deep CNN Image Classifier with ANY Images. *YouTube*. Retrieved from [https://www.youtube.com](https://www.youtube.com)*

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