kyegomez/VisionMamba
Implementation of Vision Mamba from the paper: "Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model" It's 2.8x faster than DeiT and saves 86.8% GPU memory when performing batch inference to extract features on high-res images
This project helps machine learning engineers efficiently process high-resolution images for tasks like image classification or feature extraction. It takes batches of high-resolution images as input and provides feature representations or classification predictions as output. It is designed for developers building computer vision applications who need faster and less memory-intensive image processing.
482 stars.
Use this if you are a machine learning engineer working with large datasets of high-resolution images and need to extract features or classify them with significantly reduced GPU memory consumption and faster inference times.
Not ideal if you are an end-user without programming experience or if you need a pre-trained, ready-to-use model for specific tasks like facial recognition without any custom development.
Stars
482
Forks
21
Language
Python
License
MIT
Category
Last pushed
Mar 09, 2026
Commits (30d)
0
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