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Image classification pipeline relying on Deep Learning models dealing with training over multiple datasets with unbalanced labels and classes. In addition, also classification using histogram data was done for better generalization over different datasets.

Python 100.00%
computer-vision histograms huggingface image-classification imbalanced-classes imbalanced-datasets pytorch

weatherclassification's Introduction

I'm a graduate Computer Engineering student, with a strong passion for Artificial Intelligence. Based in Rovigo, Italy but my heart will be forever in my Erasmus home Prague, Czech Republic.
Available to move anywhere in the world.

Things I code with

  • Machine Learning: Static Badge Static Badge Static Badge Static Badge Static Badge Static Badge Static Badge Static Badge Static Badge

  • Robotics: Static Badge Static Badge Static Badge

  • App Programming: Static Badge Static Badge

  • OOP: Static Badge Static Badge Static Badge

  • Web Development: Static Badge Static Badge Static Badge Static Badge Static Badge

Top projects I worked on:

An Empirical Study on Ensemble of Segmentation Approaches: (🔗Code | 📄Published Paper)

  • Empirical search of the best ensemble architecture to perform colorectal polyps segmentation
  • Tested ensembles with different backbones, data augmentation methods and loss functions
  • Personally, I implemented several loss functions to create ensembles with greater diversity
  • Achieved 0.893 Dice score
  • Developed with MATLAB

Sentiment Analysis for Climate Change: (🔗Code | ✍️Blog Article)

  • Sentiment Analysis over a tweets dataset regarding climate change with BERT and other Machine Learning techniques (e.g. SVM)
  • Category 13 of the Sustainable Development Goals defined by the United Nations: Climate Action
  • Achieved 0.86 F1-Score
  • Developed with Tensorflow, Pandas, Scikit-learn, Python

Better Quality Embeddings for Node Classification Combining Classificiation with Link Prediction in Graph Neural Networks (🔗Code | 📄Paper)

  • Implementation of Link Prediction as self-supervised pre-training technique in Graph Neural Networks for node classification.
  • Tested with GCN, GAT and GraphSAGE (with mini-batch generation) with MLP for improved learning during Link Prediction task.
  • Personally, I developed the whole training loop. My colleagues implemented grid search algorithms.
  • Developed with Pytorch, Pytorch Geometric, Python

🌤️ Multi-dataset Weather Classification: (🔗Code | 📄Report)

  • Image classification over weather images coming from multiple datasets.
  • Usage of joint training for classes having similar features across different datasets.
  • Handling of dataset and class imbalance.
  • Usage of histograms with Linear Neural Networks for testing over fully unseen dataset.
  • Achieved 0.994 Accuracy
  • Developed with Pytorch, OpenCV, Python

🐢 Sea Turtle Face Detection for Ocean Conservation: (🔗Code | 📄Report)

  • Object detection of turtles' heads with RetinaNet algorithm
  • Category 14 of the Sustainable Development Goals defined by the United Nations: Life Below Water
  • Achieved 0.878 IoU score
  • Developed with Tensorflow, Pandas, Python

Grayscale to RGB for CNN training: (🔗Code | 📄Report)

  • Conversion of 16 grayscale foraminifera images to a single image in RGB format
  • The goal is to improve the classification accuracy of a CNN, the conversion is done with unsupervised learning techniques
  • Achieved 0.805 accuracy
  • Developed with MATLAB

Where Am I? (🔗Code | 📄Report in Italian)

  • Android application to track in real-time the longitude, latitude and altitude of the device. Showing also the history of the past 5 minutes. Everything on a map as well.
  • Developed with Kotlin

and many others... Just look at my repositories.


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