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data : folder containing csv of jets events from 2011 to 2016, csv of non jet events from HEK database and the whole downloaded dataset of image sequencies.
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data collection : folder containing a jupyter notebook that shows how the dataset was created and a python file that contain helper functions for this task.
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model training : folder containing a jupyter notebook that shows how the model has been implemented and trained. Also a python file with helper functions for this task.
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model evaluation : folder that contains:
- model_analysis.ipynb : jupyter notebook that shows the result of the model
- helper_analysis.py : python file that contain helper functions for this task
- results_cv_final.json : json file that contain the results of the cross validation (18 models)
- 2 Best resulting models : Trained_RCNN.pth and Trained_RCNN_2.pth
- figures : folder that contains every missclassified events as gif, the plots displayed in the notebook
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try_model_with_your_data.ipynb : jupyter notebook for the ESA that will try new datas with our model to benefit from automatic classification and so, detection.
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animation.gif : exemple of the event 29 containing a jet and used in our paper.
The following is a summary of the libraries and their specific modules used in this project, organized by their functional category:
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Core Libraries
- NumPy
- Pandas
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Solar Physics
- Sunpy
- Astropy
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Operating System Interaction
- OS
- System
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Machine Learning Frameworks
- PyTorch
- Torchvision
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Cross-Validation and Metrics
- Scikit-Learn
- SciPy
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Visualization and Display
- Matplotlib
- IPython Display Utilities
- Seaborn
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Miscellaneous
- Random
Take care to not run cells in red (These are explicitly noted in the notebooks) because the computationnal requirement is huge.
- Make sure to have all used libraries installed.
- Start with data collection
- Then with model training
- Finally with the model evaluation.
- 6-12 November: familiarization with the project and exploration of solar physics Python libraries
- 13-30 November: development of the algorithm to download the data and save it in the correct format
- 1-17 December: design of machine learning architecture and initiation of report writing.
- 18-21 December: completion of results analysis and finalization of the report.