Skip to content

Trains segmentation model using task parallelism and data parallelism

License

Notifications You must be signed in to change notification settings

qualiphal/parallel-phal

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

12 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Qualiफल (Qualiphal)

Multi-Processing and Shared caching based Quality Assurance system for supply chain of fruit delivery and import/export.

During cleaning and preprocessing, we removed the images with labels 'image_quality' and 'condition' as we only wanted to train on high quality data. Also, we would not be using 'artifact' label in training but we will keep the images, and 'pedicel' label would go into segmentation model but not in post processing.

Processed images and masks are stored as numpy arrays in npy format. Processed masks have 6 channels (equal to number of classes defined in labels.json) and ordered in ascending order of values (category_ids)

Authors

Abhimanyu Banerjee CSE, Bennett University Gurgaon, India Ab8963@benett.edu.in

Anudit Nagar CSE, Bennett University Noida, India An9316@bennett.edu.in

Himanshu Mittal CSE, Bennett University Bahadurgarh, India Hm6729@bennett.edu.in

About

Trains segmentation model using task parallelism and data parallelism

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published