Hey friends!
We are mid-way through the GSOC journey and we have put considerable amount of work into it. The project is turning out good and we are very close to the finish line. This is where we enable DRS and move quickly!
Recently another branch was merged into Hub's main that optimized the code and replaced the npz format. So naturally, after last week's checkin I updated the code to run Hub auto smoothly.
Later last week I spent time on 2 major things: Mid season presentation
Writing tests for Hub auto
I think it went great and I received important tips from the members that would enhance the working of Hub auto.
I'll take this opportunity to share my progress on the blog. Dyllan and I have been through multiple iterations of hub auto and we have settled at feature/2.0/image-classification-auto.
I have tested hub.auto on about 200 datasets and the success rate is around 50%, soon things will turn around when I'll integrate handling of multiple file extensions in the coming weeks and this number will shoot to 80-90% 🚀
To wrap up, this week was super productive and by next week we are hoping to increase the success rate of hub-auto!
We are mid-way through the GSOC journey and we have put considerable amount of work into it. The project is turning out good and we are very close to the finish line. This is where we enable DRS and move quickly!
Recently another branch was merged into Hub's main that optimized the code and replaced the npz format. So naturally, after last week's checkin I updated the code to run Hub auto smoothly.
Later last week I spent time on 2 major things:
Mid-season presentation
This week I presented my project's progress to Activeloop members, highlighting my progress and laying out a plan to wrap up this project in the second half of GSOC.I think it went great and I received important tips from the members that would enhance the working of Hub auto.
I'll take this opportunity to share my progress on the blog. Dyllan and I have been through multiple iterations of hub auto and we have settled at feature/2.0/image-classification-auto.
I have tested hub.auto on about 200 datasets and the success rate is around 50%, soon things will turn around when I'll integrate handling of multiple file extensions in the coming weeks and this number will shoot to 80-90% 🚀
Writing tests for Hub auto
I wrote tests for hub-auto that makes the testing of hub-auto convenient. Additionally, I have added the "kaggle" functionality to hub-auto that was earlier removed. This api would allow users to download Kaggle datasets, convert them to structured datasets with just one line of code.To wrap up, this week was super productive and by next week we are hoping to increase the success rate of hub-auto!