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Monk_Object_DetectionTweet Open Source Love

A one-stop repository for low-code easily-installable object detection pipelines.


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Documentation

Pipelines presented as jupyter notebooks - see example_notebooks

(See the licenses for each pipeline and use accordingly)




Installation

  • A) GluonCV Finetune

    • Check - Monk_Object_Detection/1_gluoncv_finetune/
  • B) TorchVision Finetune

    • Check - Monk_Object_Detection/2_pytorch_finetune/
  • C) MX-RCNN

    • Check - Monk_Object_Detection/3_mxrcnn/
  • D) Efficient-Det

    • Check - Monk_Object_Detection/4_efficientdet/
  • E) Pytorch-Retinanet

    • Check - Monk_Object_Detection/5_pytorch_retinanet/
  • F) CornerNet-Lite

    • Check - Monk_Object_Detection/6_cornernet_lite/
  • G) YoloV3

    • Check - Monk_Object_Detection/7_yolov3/
  • H) RFBNet

    • Check - Monk_Object_Detection/8_pytorch_rfbnet



Author

Tessellate Imaging - https://www.tessellateimaging.com/

Check out Monk AI - (https://github.com/Tessellate-Imaging/monk_v1)

Monk features
    - low-code
    - unified wrapper over major deep learning framework - keras, pytorch, gluoncv
    - syntax invariant wrapper

Enables developers
    - to create, manage and version control deep learning experiments
    - to compare experiments across training metrics
    - to quickly find best hyper-parameters

To contribute to Monk AI or Monk Object Detection repository raise an issue in the git-repo or dm us on linkedin




Copyright

Copyright 2019 onwards, Tessellate Imaging Private Limited Licensed under the Apache License, Version 2.0 (the "License"); you may not use this project's files except in compliance with the License. A copy of the License is provided in the LICENSE file in this repository.