Week 3 : Coding period

-

What did I do this week?

  • I fine-tuned my DenseNet201 and InceptionV3 models till I could maximize their accuracy. I used class-weighting for this purpose and achieved around 85% accuracy on both the models.
  • For both the models, I calculated all the performance metrics like -
    • Accuracy
    • Loss
    • Confusion matrix
    • Precision
    • Recall
    • F1 score
    • ROC curve

      I will now document all these metrics to present them to my mentor.
  • I implemented dynamic range and Float16 quantization methods on DenseNet201 and InceptionV3 and evaluated their accuracy. Float16 gave minimum change in accuracy while dynamic range dropped the accuracy considerably.
Plans for the upcoming week

 I tried implementing Int8 quantization and model pruning but ran into a heap of errors. So this week, I will rectify those errors and make these modules run.

Earlier when I had experimented with pruning I ran into a lot of OOM errors so my mentor has been gracious to provide me with a Jupyter Hub access hosted on the PLHI server. The best part about this is that my data will be persistent and I would not have to run the complete notebook every time I need to run a single module. So I will be migrating my work from the Kaggle notebook to the PLHI server.

I was evaluating my system architecture and looking for easier methods to develop this system. Currently, the design says that I will run a Raspberry Pi from the Qemu emulator. This Raspberry Pi will host the Flask application that will test the images. The emulator is supposed to be run using a Docker container. Sounds complex? Yes it is. So I have a few more approaches in my mind to simplify this architecture for testing. I will discuss this with my mentor in our upcoming meeting.


That's all for this week folks. See you next week!
Happy coding! :)

Comments

Post a Comment

Popular posts from this blog

GSoC 2020 with LibreHealth : Final Report

-
Image
Low Powered Models for Disease Detection and Classification for Radiology Images Project Description - The aim of this project is to create Deep Learning models for detection and classification of radiology images. The models must be compressed such that they can be deployed to low powered devices like ARM devices, Android devices, etc. Compression techniques such as Quantization and Pruning can be used.     Mentors - Priyanshu Sinha Saptarshi Purkayastha Judy Gichoya Geeta Priya Padmanabhan Tech Stack -  Numpy Pandas PyDicom Tensorflow Tensorflow-Lite/ Tensorflow-Model-Optimization Docker Qemu Project Link - Click here Commits - Click here    Merge Requests - Click here Why to do this - There has been a lot of progress in developing Machine Learning models that predict the medical condition of a patient based upon specific inputs relevant to the diagnosis of that condition. However, these models have drawbacks while deployment in real-time on edge devices. Firstly, they have been trai
Read more

Week 5 : Coding period

-
Week 5 Update - Today I received the results of my first evaluation and I have passed!! Woohoo! I am happy that my mentors are happy with my work. They have asked me to improve my code by making it more reusable. So I will work on this feedback in the upcoming weeks. Work update for this week - I have (finally) successfully implemented int8 quantization. Although I had to migrate everything to Colab for this, it finally run. Even though my code is split on various platforms, I am happy that most of what I have implemented is running well on my local machine. The 2nd thing that I did this week was make python scripts for every module of my notebook. I made scripts for dataset preprocessing, model building, model training, quantization and pruning. I am now adding inference scripts for large as well as compressed models. This is the part that I have to make more reusable. I also have to cover the edge cases and erroneous inputs for these scripts. I want to present the results of
Read more

Week 1 : Acceptance and Community Bonding

-
Image
Introduction My name is Aishwarya Harpale and I am a Senior year Computer Engineering student from Pune Institute of Computer Technology, Pune. I was selected for GSoC 2020 to contribute to the Open Source Organization — LibreHealth. The project that I will be working on is “Low Powered Models for Disease Detection and Classification for Radiology Images”. I will explain a bit about my journey until now. Pre-GSoC Period After the organizations were announced, I began looking for projects that came under the category of Machine Learning. LibreHealth’s projects caught my eye and I felt that I could contribute to those projects using my knowledge. As students, we were asked to develop a Proof of Concept(PoC) so that the mentors could evaluate our coding skills. I developed an application for Classification and Localization of Chest-XRay images. This application had a front-end developed using Bootstrap and it was deployed on a Flask server. For further detai
Read more