Introduction ======================= Overview ----------------- *HyperTrack* is a new hybrid algorithm for deep learned clustering based on a learned graph constructor called Voxel-Dynamics, Graph Neural Networks and Transformers. For more details, see the paper and the conference talk. This repository together with pre-trained torch models downloaded from Hugging Face can be used to reproduce the paper results on the charged particle track reconstruction problem. Hugging Face Quick Start ------------------------------ Install the framework, process TrackML dataset files, download the pre-trained models from Hugging Face https://huggingface.co/mieskolainen and follow the documentation for inference. TrackML dataset ------------------------------ Install first the Kaggle API as instructed in https://www.kaggle.com/docs/api and download TrackML challenge data: .. code-block:: none cd .. && mkdir trackml && cd trackml kaggle competitions download -c trackml-particle-identification -f train_1.zip kaggle competitions download -c trackml-particle-identification -f train_2.zip kaggle competitions download -c trackml-particle-identification -f train_3.zip kaggle competitions download -c trackml-particle-identification -f train_4.zip kaggle competitions download -c trackml-particle-identification -f train_5.zip kaggle competitions download -c trackml-particle-identification -f test.zip unzip train_1.zip unzip train_2.zip unzip train_3.zip unzip train_4.zip unzip train_5.zip unzip test.zip Then execute the following to convert events into pickle files: .. code-block:: none source tests/process_trackml.sh Folder structure ----------------------- .. code-block:: none - docs : Documentation - data : Pickle files of input data - figs : Training and prediction figures - models : Trained torch models - hypertrack : Main source code - hypertrack/models : Model definitions and hyperparameters - src : Training and inference steering code - tests : Launch and test scripts