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

Monk_Object_Detection

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

Documentation

• LINK

Pipelines presented as jupyter notebooks - see example_notebooks

(See the licenses for each pipeline and use accordingly)

• A) GluonCV Finetune: Original - https://gluon-cv.mxnet.io/build/examples_detection/index.html

  • SSD with Vgg16
  • SSD with Resnet50
  • SSD with Resnet101
  • SSD with MobileNet1.0
  • YoloV3 with Darknet53
  • YoloV3 with MobileNet1.0

• B) TorchVision Finetune: Original - https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html

  • Faster-RCNN with MobileNet2.0

• C) MX-RCNN: Original - https://github.com/ijkguo/mx-rcnn

  • Faster-RCNN with VGG16
  • Faster-RCNN with Resnet50
  • Faster-RCNN with Resnet101

• D) Efficient-Det: Original - https://github.com/signatrix/efficientdet

• E) Pytorch-Retinanet: Original - https://github.com/yhenon/pytorch-retinanet

  • Resnet18
  • Resnet34
  • Resnet50
  • Resnet101
  • Resnet152

• F) CornerNet-Lite: Original - https://github.com/princeton-vl/CornerNet-Lite

  • CornerNet-Saccade
  • CornerNet-Squeeze

• G) YOLOV3: Original - https://github.com/ultralytics/yolov3

  • yolov3
  • yolov3s
  • yolov3-spp
  • yolov3-spp3
  • yolov3-tiny
  • yolov3-spp-matrix
  • csresnext50-panet-spp

• H) RFBNet: Original - https://github.com/ruinmessi/RFBNet

  • VGG16
  • E_VGG16
  • MobileNet

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

• Abhishek - https://www.linkedin.com/in/abhishek-kumar-annamraju/
• Akash - https://www.linkedin.com/in/akashdeepsingh01/

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.