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Metadata of Lacmus dataset and trained model weitghts tracked with dvc

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lacmus-foundation/ladd-and-weights

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Metadata of Lacmus dataset and trained model weitghts tracked with dvc.

Usage

Repository in intended to sustain reproducability and versioning of the dataset and model weights.

  • Install our fork ofdvc, becouse original dvc has anissue
conda create -n lacmus-dvc python==3.9 pipconda activate lacmus-dvcgit clone https://github.com/lacmus-foundation/dvc.gitcd dvcpip install .
  • In order to get access to cloud storage, contact lacmus team as described on wiki and get yourcredentials file.
  • Put credentials to~/.aws/
  • Configure dvc and get data:
git clone https://github.com/lacmus-foundation/ladd-and-weights.gitcd ladd-and-weightsdvc remote modify --local digital_ocean credentialpath ~/.aws/credentialsdvc pull

Contributing

In case you'll add new imagesSet, please also update dvc.yaml and gather_LADD.sh

Project structure

\- dataset-- pretrain--- sdd-lacmus-version - Prepared Standford Dron Dataset--- VisDrone2019-DET VisDone Dataset-- LADD---...  folders part of dataset, gathered as project evolves --- -- unmarked---... folders of file sets, submitted by users, but not yet included into main dataset----- 3rd_party--- heridal - http://ipsar.fesb.unist.hr/HERIDAL%20database.html (*) - weights -- yolo5 - wieghts for yolo v5 (https://github.com/ultralytics/yolov5) currently in production -- keras-retinanet - weights for keras retina-net model (https://github.com/lacmus-foundation/lacmus/tree/master/keras_retinanet) - previous version of network-- torch--- pretrain--- experimental

(*) Dunja Božić-Štulić, Željko Marušić, Sven Gotovac: Deep Learning Approach on Aerial Imagery in Supporting Land Search and Rescue Missions, International Journal of Computer Vision, 2019.

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Metadata of Lacmus dataset and trained model weitghts tracked with dvc

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