SaptakBhoumik/TinyVision
TinyVision is an evolving project focused on designing ultra-lightweight image classification models with minimal parameter counts. The goal is to explore what’s actually necessary for fundamental vision tasks by combining handcrafted feature preprocessing with highly efficient CNN architectures.
This project helps machine learning researchers and engineers explore how to build very small, efficient image classification models. It takes in image datasets, like those for classifying cats vs. dogs or CIFAR-10, and outputs highly compact neural network models that can still achieve good accuracy. The primary users are those focused on minimizing model size and computational resources for computer vision tasks.
No commits in the last 6 months.
Use this if you are a machine learning researcher or engineer interested in designing and experimenting with ultra-lightweight image classification models for resource-constrained environments.
Not ideal if you are looking for a ready-to-use, production-grade image classification solution or a library for general-purpose computer vision tasks beyond lightweight model experimentation.
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Language
Jupyter Notebook
License
CC-BY-SA-4.0
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Last pushed
Aug 06, 2025
Commits (30d)
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