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# Quark-Gluon Classification This repository contains a deep learning solution for classifying 125x125 matrices in three-channel images of quarks and gluons impinging on a calorimeter. The task involves building and evaluating two different deep learning models: a VGG model with 12 layers and a ResNet-152 model.

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Rohanberiwal/Quark-Glucon-Classifier-with-Hadronic-calorimeters

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Quark-Gluon Classification

This repository contains a deep learning solution for classifying 125x125 matrices in three-channel images of quarks and gluons impinging on a calorimeter. The task involves building and evaluating two different deep learning models: a VGG model with 12 layers and a ResNet-152 model.

Dataset

The dataset is available at: Quark-Gluon Dataset

Dataset Description

  • Type: 125x125 matrices in three-channel images.
  • Classes: Two classes of particles - Quarks and Gluons.

Models

1. VGG Model

  • Architecture: VGG with 12 layers.
  • Modification: Reduced weights in the fully connected (FC) layers to speed up training and optimized architecture for classification.

2. ResNet-152 Model

  • Architecture: ResNet-152.
  • Details: Utilizes a very deep residual network with 152 layers to achieve high classification performance.

Training and Evaluation

Data Splitting

  • Training Set: 80% of the data.
  • Validation Set: 20% of the data.

Training

  • Both models are trained on the training set.
  • Evaluated on the validation set to ensure no overfitting.

Evaluation

  • The performance of each model is assessed based on classification accuracy.
  • Model weights are saved and can be found in the weights/ directory.

Code

The code for training and evaluating the models is provided in the Jupyter notebook Quark_Gluon_Classification.ipynb. The notebook includes:

  • Data preprocessing and augmentation.
  • Model definitions (VGG-12 and ResNet-152).
  • Training loops.
  • Evaluation and result visualization.

Requirements

  • Python 3.x
  • PyTorch
  • torchvision
  • numpy
  • matplotlib
  • PIL

Install the required libraries using:

pip install torch torchvision numpy matplotlib pillow

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# Quark-Gluon Classification This repository contains a deep learning solution for classifying 125x125 matrices in three-channel images of quarks and gluons impinging on a calorimeter. The task involves building and evaluating two different deep learning models: a VGG model with 12 layers and a ResNet-152 model.

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