ZhiyuanDang/NNM
The PyTorch official implementation of the CVPR2021 Poster Paper NNM: Nearest Neighbor Matching for Deep Clustering.
This project helps machine learning researchers or practitioners in computer vision to automatically group similar images together without needing pre-labeled data. It takes a collection of images as input and organizes them into meaningful clusters based on their visual features, identifying underlying categories. The output is a set of clustered images and can include visualizations of representative images for each group.
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Use this if you need to discover natural groupings within an unlabeled image dataset, such as categorizing product images, scientific samples, or surveillance footage, to gain insights or prepare for further analysis.
Not ideal if you already have labeled data and need to perform supervised classification, or if your primary goal is object detection or image generation.
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Language
Python
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Last pushed
Dec 15, 2021
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