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Monk - A computer vision toolkit for everyone

Monk Object Detection - A low code wrapper over state-of-the-art deep learning algorithms

Why use Monk

• Issue: Abudance of algorithms and difficult to find a working code
  • <b> Solution: All your state-of-the-art as well as old algorithms in one place </b>
• Issue: Installaing different deep learning pipelines is an error-prone task
  • <b> Solution: Single line installations with monk </b>
• Issue: Setting up different algorithms for your custom data requires a lot of effort in changing the existing codes
  • <b> Solution: Easily ingest your custom data for training in COCO, VOC, or Yolo formats </b>
• Issue: Difficulty to trace out which hyperparameters to change for tuning the algorithm
  • <b> Solution: Set your hyper-parameters with a common structure for every algorithm </b>
• Issue: Deployment requires knowledge of base libraries and codes
  • <b> Solution: Easily deploy your models using Monk's low code-syntax </b>
• Issue: Looking for hands-on tutorials for computer vision
  • <b> Solution: Use monk's application building tutorial set</b>

Create real-world Object Detection applications

For more such tutorials visit Application Model Zoo

Create real-world Image Segmentation applications

For more such tutorials visit Application Model Zoo

Other applications

For more such tutorials visit Application Model Zoo

Important Elements

• A) Training Engine
  • Train models on custom dataset witjh low code syntax
  • Pretrained examples on variety of datasets
  • Useful to train your own detector
• B) Inference Engine
  • Original pretrained models (from original authors and implementations) for inferencing and analysing
  • Pretrained models on coco, voc, cityscpaes, type datasets
  • Useful to analyse which algorithm works best for you
  • Useful to generate semi-accurate annotations (coco, pascal-voc, yolo formats) on a new dataset

Training Engine Algorithms

- Train models on custom dataset with low code syntax
- Pretrained examples on variety of datasets
- Useful to train your own detector

NOTE - See the licence file mentioned in the pipelines before using them

Inference Engine Algorithms

- Infer already trained models on COCO/VOC/Open-Images on your custom data
- Useful to analyse computation time metrics

Aknowledgements

• Contributors' information can be found here: https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/Contributors.md
• Majority of the code is obtained from these pipelines (Monk is a low code wrapper to utilize these pipelines)
  • https://gluon-cv.mxnet.io/build/examples_detection/index.html
  • https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1.md
  • https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2.md
  • https://github.com/signatrix/efficientdet
  • https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch
  • https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html
  • https://github.com/ijkguo/mx-rcnn
  • https://github.com/yhenon/pytorch-retinanet
  • https://github.com/princeton-vl/CornerNet-Lite
  • https://github.com/ultralytics/yolov3
  • https://github.com/ruinmessi/RFBNet
  • https://github.com/PengyiZhang/SlimYOLOv3
  • https://github.com/qfgaohao/pytorch-ssd
  • https://github.com/open-mmlab/mmdetection
  • https://github.com/qubvel/segmentation_models
  • https://github.com/biubug6/Pytorch_Retinaface
  • https://github.com/open-mmlab/mmaction2
  • https://github.com/WXinlong/SOLO
  • https://gluon-cv.mxnet.io/build/examples_pose/dive_deep_simple_pose.html
  • https://github.com/Cartucho/mAP

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/ <br />

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.