matlab-deep-learning/MATLAB-Deep-Learning-Model-Hub

What it solves

This repository provides a centralized hub for discovering and accessing pretrained deep learning models compatible with MATLAB. It eliminates the need for users to manually search for and convert models by providing a curated list of networks across various modalities, which can be used for immediate inference or as starting points for transfer learning.

How it works

The hub acts as a directory of pretrained networks. Users can access these models through MATLAB functions (such as imagePretrainedNetwork) or by following links to specific GitHub repositories and documentation. The models are categorized by task, providing details on network size, accuracy, and the specific datasets they were trained on (e.g., COCO for object detection or PASCAL VOC for semantic segmentation).

Who it’s for

It is designed for MATLAB users, researchers, and engineers who want to integrate deep learning capabilities into their workflows without training models from scratch.

Highlights

• Broad Modality Support: Includes models for computer vision (classification, detection, segmentation, pose estimation), natural language processing (Transformers), audio (speech-to-text, sound classification), Lidar (point cloud analysis), and robotics (motion planning).
• Transfer Learning Ready: Many models are specifically highlighted as starting points for training custom detectors or classifiers.
• Task-Specific Models: Offers both general-purpose models and application-specific ones, such as lane detection, road boundary detection, and brain tumor segmentation.
• Diverse Architectures: Features a wide range of architectures including YOLO (v2 through v9), ResNet, EfficientNet, Vision Transformers, and GANs.