GT-RIPL/L2C
Learning to Cluster. A deep clustering strategy.
This project helps machine learning researchers and practitioners perform clustering tasks on image data, especially when traditional labels are scarce or unavailable. It takes raw image datasets and outputs clustered image groups, leveraging advanced deep learning techniques. This tool is for those working on computer vision problems like image recognition or segmentation.
316 stars. No commits in the last 6 months.
Use this if you need to group similar images together without extensive pre-labeled data, for tasks like categorizing diverse handwritten characters or segmenting objects in complex scenes.
Not ideal if your primary goal is standard supervised classification with abundant labeled data, or if you are working with non-image data types.
Stars
316
Forks
46
Language
Python
License
MIT
Category
Last pushed
Jan 08, 2020
Commits (30d)
0
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