https://github.com/praeclarumjj3/atlas_evaluation
Submission for the Evaluation Exercise for ATLAS Encoders
https://github.com/praeclarumjj3/atlas_evaluation
autoencoder data-compression fastai pytorch
Last synced: about 1 month ago
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Submission for the Evaluation Exercise for ATLAS Encoders
- Host: GitHub
- URL: https://github.com/praeclarumjj3/atlas_evaluation
- Owner: praeclarumjj3
- Created: 2021-03-12T09:44:48.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-03-12T14:16:57.000Z (about 5 years ago)
- Last Synced: 2025-12-26T18:48:24.762Z (5 months ago)
- Topics: autoencoder, data-compression, fastai, pytorch
- Language: Jupyter Notebook
- Homepage:
- Size: 1.42 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ATLAS Evaluation
[**[Presentation]**](https://docs.google.com/presentation/d/1TEzEqilbNTESiVPPP8BjSedZ-YLAT2OCrAgii--jEik/edit?usp=sharing)
This repo contains the code for my submission of the task given by mentors for the GSoC 2021 Project: [Deep autoencoders for ATLAS data compression](https://hepsoftwarefoundation.org/gsoc/2021/proposal_ATLASCompressionAE.html) under [**CERN-HSF**](https://summerofcode.withgoogle.com/organizations/4526188451594240/) Organization.
**Problem Statement**: Prepare an autoencoder to compress the four-momentum of a sample of simulated ‘j’ particles from 4 to 3 variables for the dataset available on [drive](https://drive.google.com/file/d/1MJePFQT3OKdnmdlaJPNWwxqWrGEbj2F_/view).
## Contents
1. [Setup Instructions](#1-setup-instructions)
2. [Repository Overview](#2-repository-overview)
3. [Results](#3-results)
## 1. Setup Instructions
- Clone the repo:
```
git clone https://github.com/praeclarumjj3/ATLAS_Evaluation.git
```
- To run the AE Compression model and see the results, open [AE_Compression_3D.ipynb](https://github.com/praeclarumjj3/ATLAS_Evaluation/blob/master/AE_Compression_3D.ipynb) and run it cell by cell.
## 2. Repository Overview
The repository is structured as follows:
- `data` - Contains the datas files for training/testing the AE Compression model.
- `plotInput` - Contains images of the plots for `normalized` Input Data.
- `plotOutput` - Contains images of the plots for `normalized` Input Data.
- `prepare_datset.py` - Script for cleaning and preparing the `.pkl` file which contains the data used for training.
- `AE_Compression_3D.ipynb` - Contains code for running the model.
## 3. Results
- The compression model works well on the given dataset as can be seen from the overlapping output-input plots for the normalized data:
