Awesome Computer Vision Models
A curated list of popular deep learning models for image classification, segmentation, and detection with key performance metrics.
What is Awesome Computer Vision Models?
Awesome Computer Vision Models is a curated GitHub repository that aggregates and organizes popular deep learning models for image classification, segmentation, and object detection. It provides a structured reference with key performance metrics like parameter counts, FLOPs, and accuracy scores, helping researchers and engineers quickly compare and select models for their projects.
Computer vision researchers, machine learning engineers, and students who need a consolidated reference for model architectures and their performance on standard benchmarks like ImageNet, COCO, and Pascal VOC.
It saves significant research time by compiling hundreds of models with consistent metrics in one place, offering a clear historical and technical overview that is often scattered across papers and blogs.
Overview
A list of popular deep learning models related to classification, segmentation and detection problems
Use Cases
Best For
- Comparing model efficiency (parameters vs. accuracy) for deployment
- Researching the evolution of computer vision architectures
- Selecting a baseline model for image classification tasks
- Finding segmentation models with high mIOU scores
- Identifying real-time object detectors for embedded systems
- Studying performance metrics across different datasets and years
Not Ideal For
- Teams needing ready-to-use code or pre-trained models for immediate deployment
- Researchers requiring the most up-to-date model performances on cutting-edge benchmarks beyond 2021
- Practitioners looking for implementation tutorials, training scripts, or deployment guides
- Projects focused on niche computer vision tasks not covered by standard classification, segmentation, or detection benchmarks
Pros & Cons
Pros
Organizes hundreds of models into clear tables for classification, segmentation, and detection, as shown in the README's separate sections with comparative data.
Includes key metrics like parameters, FLOPs, and accuracy scores (e.g., Top-1 Error, mAP) from papers, allowing direct efficiency comparisons across models.
Tracks evolution from foundational works like AlexNet (2014) to modern architectures like Vision Transformers (2021), providing context for model development trends.
Each model entry links to its seminal research paper, facilitating deeper study and verification of metrics, as evidenced by arXiv URLs in the tables.
Cons
Many entries have missing metrics (marked with '?'), such as FLOPs for VGG-16 or specific error rates, which reduces comparability and reliability.
As a static GitHub repository, it may not include models or benchmarks beyond 2021, with no indication of regular updates, requiring users to supplement with current research.
No code examples, training scripts, or deployment guides are provided; users must find implementations elsewhere, limiting immediate utility for hands-on projects.
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