Google Summer of Code with LibreHealth

This week I had a video meeting with my mentor Priyanshu Sinha. We discussed a lot of things and I was able to clear most of my queries. 1. I discussed how the datasets suggested earlier were not open source and hence, I would be switching to other open source options. 2. We finalized all the model compression methods that I would be trying. I will be majorly focusing on post-training methods but at the same time, I was free to experiment with compression aided methods. 3. We restructured the system architecture and decided the functionalities of the Flask application. It will run on the Raspberry Pi emulated using Qemu. Qemu will be run using a docker container. I have successfully done 3/4th of this. The integration of the Flask application is challenging and will be done later. 4. Based on the previous point, I modified my Dockerfile and created my first Merge Request. 5. I mapped out a workflow for my project.  First I will begin with model training and then proceed to m

Update : In this week, I did the following tasks. Prepared Dockerfile to send a merge request. This needed to be integrated with the previous Dockerfile.  Implemented sample quantization and pruning methods on Densenet201 model. Quantization turned out to be fine but pruning made me run into Out-Of-Memory (OOM) errors multiple times. Prepared my system for the project by installing all the required packages. I had to uninstall my Ubuntu system and dual boot my PC again with an increased partition size so that it could accommodate the size of the project as well as the Docker images. Finalized model performance methods and packages for it. I found the psutil python library the could help me perform model evaluation. Read about Knowledge Distillation and it’s related papers. This concept took time to understand and I found more interesting methods for model compression. Following are my goals for next week. These are urgent tasks and need to be done asap. Finalize datasets,

Update : This week, I received a brief overview of instructions from my mentor and began working on them. I had finished reading about Quantization and Pruning of Models. I referred to the following videos for a brief overview apart from my other reading material. TFWorld session by Raziel Alvarez - https://youtu.be/3JWRVx1OKQQ Inside Tensorflow session by Suharsh Sivakumar - https://youtu.be/4iq-d2AmfRU I also emulated a Raspberry Pi device using Qemu emulator inside a docker container. Being new to Docker and Qemu, I faced a few errors but in the end, I was able to do this task successfully. This week I will read a few papers that my mentors have mentioned. My work will include building upon the work performed by the researchers in the papers. I also plan to set up a detailed plan for the summer with my mentor.

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