Slides from the webinar on Challenges of Deep Learning in Computer Vision presented by Tessellate Imaging and powered by E2E Networks. The webinar discusses the growth and applications of Computer Vision in modern-day real life. Challenges with implementing and developing Deep Learning and Computer Vision projects for both enterprises and developers. We introduce MonkAI (https://monkai.org) an Open Sourced Deep Learning wrapper library for Computer Vision development and talk about features tackling some of the challenges in Deep Learning.

2. How do we see?Why?
Source : https://idyll.pub/post/the-eye-5b169094cce3bece5d95e964/

3. Early applications of Image
processing
- noise removal
- media compression
- medical imaging
- manufacturing
Source : https://progmohamedali.wordpress.com/2014/02/24/image-filters-noise-removal-in-image-processing/

6. Source : Gartner Symposium India Build AI Business Case 2019
Source : The Forrester New Wave™ - Computer Vision Platforms, Q4 2019
$49Bn Industry by 2023
Growing at a rate of 32% CAGR

7. Source : https://hai.stanford.edu/sites/g/files/sbiybj10986/f/ai_index_2019_report.pdf

8. Challenges with Deep Learning Projects

10. Background
Designed By Computer Vision
Professionals And Consultants
We have been in the domain of Computer Vision
industry for the past 7 years, working with a broad
spectrum of imaging modalities. Our teams have
served clients across the globe solving
computational challenges for Computer Vision
products. From our learnings, we bring to you a set
of easy to use tools for building Computer Vision
applications.

11. Dataset
Algorithm
(Train + Infer)
Deploy
Computer Vision + Deep Learning
What does it require
Compute Power

12. Let’s check out some state of the art work in the
domain of Computer Vision
https://paperswithcode.com/sota

13. Efficient Det
(TF)
Cornernet
(Pytorch)
● Implemented using separate base frameworks Tensorflow & Pytorch
● Different set of dependencies and setup instructions
● Leads to more time spent in prototyping and experimentation
● Working on different projects is of immense cognitive load

14. Are paid tools an alternative?

15. MonkAI
Simple. Invariant. Unified.
Standardising Computer Vision Workflows
Powered by Deep Learning Algorithms

16. MonkAI
● Standard syntax, unified wrapper. Current Support : Pytorch, Keras, MXNet
● Transfer Learning based custom Image Classification
● SOTA Deep Neural Network based Object Detection workflows
● Custom Neural Network Building and Debugging
● Monk-Studio - GUI based Deep Learning
Coming Soon -- One-click deploy to cloud, GPU optimisations, Image segmentations, GANs, support for
multiple imaging modalities, paper to code and many more.

17. Image Classification
Image Classification -- Pytorch Demo
- Create Projects and Experiments
- Prepare Dataset (Using foldered or CSV labelled
ground truth)
- Select pre-trained Deep Neural Network
- Resume experiments from the last epoch
- Apply layers, activations, tune hyperparameters.
- Compare experiments to select the best algorithm
- Infer on single or batch of images
Blogs Tutorials

18. Custom Neural
Network Builder

19. Object Detection
- Easy to Set Up
- Finetune Deep Neural Networks
- Github Repo
- Documentation and Tutorials
Existing Features and available options

20. Monk-Studio
- Github
- Image Classification Demo
- Object Detection Demo

21. Going ahead what’s the plan? --
- https://li8bot.github.io/monkai/#/home/demos
- https://li8bot.github.io/monkai/#/home/detection/tutorials
- https://github.com/Tessellate-Imaging
What next?

22. How should students go about building skills?
Some rare lectures available on Youtube :
- Image Processing : EENG 512 - Computer Vision -- Colorado School of Mines, Golden,
Colorado
- Computer Vision : The ancient secrets of Computer Vision

23. Feel free to reach out to any of our social media channels
Linkedin : tessellate-imaging
Twitter : @tessellate_img
Github : http://bit.ly/monkai-github
Website : https://monkai.org
https://www.tessellateimaging.com
Thank You!