Monk - A computer vision toolkit for everyone 
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
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 algoeithm 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 witjh low code syntax
- Pretrained examples on variety of datasets
- Useful to train your own detector
| S.No. | Algorithm Type | Algorithm | Model variations | Installation | Example Notebooks | Code | Credits | Functional Docs |
|---|---|---|---|---|---|---|---|---|
| 1 | Object Detection | GluonCV Finetune | 5 | LINK | LINK | LINK | LINK | LINK |
| 2 | Object Detection | Tensorflow Object Detection 1.0 | 22 | LINK | LINK | LINK | LINK | In Development |
| 3 | Object Detection | Tensorflow Object Detection 2.0 | 26 | LINK | LINK | LINK | LINK | In Development |
| 4 | Object Detection | Pytorch Efficient-Det 1 | 1 | LINK | LINK | LINK | LINK | LINK |
| 5 | Object Detection | Pytorch Efficient-Det 2 | 8 | LINK | LINK | LINK | LINK | In Development |
| 6 | Object Detection | TorchVision Finetune | 1 | LINK | LINK | LINK | LINK | LINK |
| 7 | Object Detection | Mx-RCNN | 3 | LINK | LINK | LINK | LINK | LINK |
| 8 | Object Detection | Pytorch-Retinanet | 5 | LINK | LINK | LINK | LINK | LINK |
| 9 | Object Detection | CornerNet Lite | 2 | LINK | LINK | LINK | LINK | LINK |
| 10 | Object Detection | YoloV3 | 7 | LINK | LINK | LINK | LINK | LINK |
| 11 | Object Detection | RFBNet | 3 | LINK | LINK | LINK | LINK | LINK |
| 12 | Object Detection | Slim-Yolo-V3 | 1 | LINK | LINK | LINK | LINK | In Development |
| 13 | Object Detection | Pytorch SSD | 3 | LINK | LINK | LINK | LINK | In Development |
| 14 | Image Segmentation | Segmentation Models | 4 | LINK | LINK | LINK | LINK | In Development |
Inference Engine Algorithms
- Original pretrained models (from original authors and implementations) for inferencing and analysing
- Pretrained models on coco, voc, cityscpaes, type datasets
- Useful to analyse which algoeithm works best for you
- Useful to generate semi-accurate annotations (coco, pascal-voc, yolo formats) on a new dataset
| S.No. | Algorithm Type | Algorithm | Model Valriations | Model Trained On | Installation | Example Notebook | Code | Credits | Functional Docs |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Object Detection | GluonCV Finetune | 4 | COCO; Pascal VOC | LINK | LINK | LINK | LINK | In Development |
| 2 | Object Detection | Pytorch EfficientDet | 8 | COCO | LINK | LINK | LINK | LINK | In Development |
| 3 | Object Detection | Detecto-RS | 2 | COCO | LINK | LINK | LINK | LINK | In Development |
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.