This repository serves as a comprehensive hub for discovering and utilizing pretrained deep learning models within MATLAB. It caters to researchers, engineers, and practitioners in computer vision, natural language processing, audio analysis, and robotics, offering a curated collection of models to accelerate development and enable transfer learning.

How It Works

The hub provides access to a wide array of pretrained neural networks, categorized by application domain. Each model entry includes details on its architecture, size, performance metrics (e.g., accuracy, mAP), and supported object classes. Users can leverage these models directly within MATLAB for tasks like image classification, object detection, semantic segmentation, and more, or use them as a foundation for custom training.

Quick Start & Requirements

• Models are accessed via MATLAB functions like imagePretrainedNetwork and audioPretrainedNetwork (since R2024a).
• Requires a MATLAB installation with the Deep Learning Toolbox.
• Specific models may have additional dependencies or require downloading large datasets.
• Refer to official documentation for detailed usage: MATLAB Deep Learning Model Hub Documentation

Highlighted Details

• Extensive coverage across Computer Vision (classification, detection, segmentation, translation, pose estimation, 3D reconstruction, video classification, text detection/recognition), NLP (Transformers), Audio (embeddings, classification, pitch estimation, speech-to-text), Robotics (motion planning), and Lidar processing.
• Includes popular architectures like ResNet, MobileNet, YOLO variants, EfficientDet, BERT, GPT-2, and specialized models like DeepLabv3+, Mask R-CNN, and NeRF.
• Provides performance benchmarks (accuracy, mAP, WER) and size information to aid model selection.
• Offers guidance on choosing models based on tradeoffs between accuracy, speed, and size.

Maintenance & Community

• Managed by MathWorks.
• Model requests can be submitted via GitHub issues or by contacting the Deep Learning Product Manager.

Licensing & Compatibility

• The models themselves are typically subject to their original licenses, which are often permissive for research and commercial use.
• MATLAB's licensing applies to the use of the software and toolboxes.

Limitations & Caveats

• Access to models requires a MATLAB license and the Deep Learning Toolbox.
• Some models may have specific version requirements or require manual downloading.
• The README notes that since R2024a, direct function calls like imagePretrainedNetwork are preferred over older methods.