Awesome Image Classification
A curated list of deep learning image classification papers and their code implementations since 2014.
What is Awesome Image Classification?
Awesome Image Classification is a curated GitHub repository that aggregates seminal academic papers and their associated code implementations for deep learning-based image classification models. It provides a historical timeline of architectures from 2014 onward, serving as a centralized educational and research resource to help developers and researchers understand the evolution of the field and quickly access implementations.
Computer vision researchers, machine learning engineers, students, and developers who need a structured overview of image classification literature and want to find or compare implementations of specific models like ResNet, EfficientNet, or Vision Transformers.
It saves significant time in literature review and code searching by providing a single, well-organized source for both papers and code. Unlike generic paper lists, it focuses specifically on image classification, includes performance benchmarks, and offers practical learning pathways for beginners.
Overview
A curated list of deep learning image classification papers and codes
Use Cases
Best For
- Researchers conducting literature reviews on image classification architectures
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