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





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

Create real-world Object Detection applications

Wheat detection in field Detection in underwater imagery Trash Detection
Object detection in bad lighting Tiger detection in wild Person detection in infrared imagery
### For more such tutorials visit [Application Model Zoo](

Create real-world Image Segmentation applications

Road Segmentation in satellite imagery Ultrasound nerve segmentation

For more such tutorials visit Application Model Zoo

Other applications

Face Detection Pose Estimation Activity Recognition
Object Re-identification Scene Text Localization Object Tracking

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

S.No.Algorithm TypeAlgorithmModel variationsInstallationExample NotebooksCodeCreditsOriginal Usage LicenseFunctional Docs
1Object DetectionGluonCV Finetune5LINKLINKLINKLINKApache 2.0LINK
2Object DetectionTensorflow Object Detection 1.022LINKLINKLINKLINKApache 2.0In Development
3Object DetectionTensorflow Object Detection 2.026LINKLINKLINKLINKApache 2.0In Development
4Object DetectionPytorch Efficient-Det 11LINKLINKLINKLINKMITLINK
5Object DetectionPytorch Efficient-Det 28LINKLINKLINKLINKLGPL 3.0In Development
6Object DetectionTorchVision Finetune1LINKLINKLINKLINKBSD-3-ClauseLINK
8Object DetectionPytorch-Retinanet5LINKLINKLINKLINKApache 2.0LINK
9Object DetectionCornerNet Lite2LINKLINKLINKLINKBSD-3-ClauseLINK
12Object DetectionSlim-Yolo-V31LINKLINKLINKLINKLicense Not AvailableIn Development
13Object DetectionPytorch SSD3LINKLINKLINKLINKMITIn Development
14Object DetectionPytorch-Peleenet1LINKLINKLINKLINKLicense Not AvailableIn Development
15Object DetectionMM-Detection36LINKLINKLINKLINKApache 2.0In Development
16Image SegmentationSegmentation Models4LINKLINKLINKLINKMITIn Development
17Pytorch RetinafaceFace Detection2LINKLINKLINKLINKMITIn Development
18Action RecognitionMM-Action28LINKLINKLINKLINKApache 2.0In Development
19Text LocalizationPytorch-TextSnake1LINKLINKLINKLINKMITIn Development
20Image SegmentationSOLO - V1/V214LINKLINKLINKLINKAcademic non-commercial usageIn Development
21Image SegmentationMask-RCNN (MMDetect)8LINKLINKLINKLINKApache 2.0In Development
22Pose EstimationGluonCV Pose11LINKLINKLINKLINKApache 2.0

Inference Engine Algorithms

- Infer already trained models on COCO/VOC/Open-Images on your custom data
- Useful to analyse computation time metrics
S.No.Algorithm TypeAlgorithmModel ValriationsModel Trained OnInstallationExample NotebookCodeCreditsFunctional Docs
1Object DetectionGluonCV Finetune4COCOPascal VOCLINKLINKLINKLINK
2Object DetectionPytorch EfficientDet8COCOLINKLINKLINKLINKIn Development
3Object DetectionDetecto-RS2COCOLINKLINKLINKLINKIn Development



Tessellate Imaging -

Check out Monk AI - (

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 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.

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