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MATLAB Deep Learning Model Hub

Discover pretrained models for deep learning in MATLAB.

Models

Computer Vision

Natural Language Processing

Audio

Lidar

Robotics

Image Classification

Pretrained image classification networks have already learned to extract powerful and informative features from natural images. Use them as a starting point to learn a new task using transfer learning.

Inputs are RGB images, the output is the predicted label and score:

These networks have been trained on more than a million images and can classify images into 1000 object categories.

Models available in MATLAB:

Note 1: Since R2024a, please use the imagePretrainedNetwork function instead and specify the pretrained model. For example, use the following code to access googlenet:

[net, classes] = imagePretrainedNetwork("googlenet");
Network Size (MB) Classes Accuracy % Location
googlenet1 27 1000 66.25 Doc
GitHub
squeezenet1 5.2 1000 55.16 Doc
alexnet1 227 1000 54.10 Doc
resnet181 44 1000 69.49 Doc
GitHub
resnet501 96 1000 74.46 Doc
GitHub
resnet1011 167 1000 75.96 Doc
GitHub
mobilenetv21 13 1000 70.44 Doc
GitHub
vgg161 515 1000 70.29 Doc
vgg191 535 1000 70.42 Doc
inceptionv31 89 1000 77.07 Doc
inceptionresnetv21 209 1000 79.62 Doc
xception1 85 1000 78.20 Doc
darknet191 78 1000 74.00 Doc
darknet531 155 1000 76.46 Doc
densenet2011 77 1000 75.85 Doc
shufflenet1 5.4 1000 63.73 Doc
nasnetmobile1 20 1000 73.41 Doc
nasnetlarge1 332 1000 81.83 Doc
efficientnetb01 20 1000 74.72 Doc
ConvMixer 7.7 10 - GitHub
Vison Transformer Large-16 - 1100
Base-16 - 331.4
Small-16 - 84.7
Tiny-16 - 22.2
1000 Large-16 - 85.59
Base-16 - 85.49
Small-16 - 83.73
Tiny-16 - 78.22
Doc

Tips for selecting a model

Pretrained networks have different characteristics that matter when choosing a network to apply to your problem. The most important characteristics are network accuracy, speed, and size. Choosing a network is generally a tradeoff between these characteristics. The following figure highlights these tradeoffs:

Figure. Comparing image classification model accuracy, speed and size.

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Object Detection

Object detection is a computer vision technique used for locating instances of objects in images or videos. When humans look at images or video, we can recognize and locate objects of interest within a matter of moments. The goal of object detection is to replicate this intelligence using a computer.

Inputs are RGB images, the output is the predicted label, bounding box and score:

These networks have been trained to detect 80 objects classes from the COCO dataset. These models are suitable for training a custom object detector using transfer learning.

Network Network variants Size (MB) Mean Average Precision (mAP) Object Classes Location
EfficientDet-D0 efficientnet 15.9 33.7 80 GitHub
YOLO v9 yolo9t
yolo9s
yolo9m
yolo9c
yolo9e
7.5
25
67.2
85
190
38.3
46.8
51.4
53.0
55.6
80 GitHub
YOLO v8 yolo8n
yolo8s
yolo8m
yolo8l
yolo8x
10.7
37.2
85.4
143.3
222.7
37.3
44.9
50.2
52.9
53.9
80 GitHub
YOLOX YoloX-s
YoloX-m
YoloX-l
32
90.2
192.9
39.8
45.9
48.6
80 Doc
GitHub
YOLO v4 yolov4-coco
yolov4-tiny-coco
229
21.5
44.2
19.7
80 Doc
GitHub
YOLO v3 darknet53-coco
tiny-yolov3-coco
220.4
31.5
34.4
9.3
80 Doc
YOLO v2 darknet19-COCO
tiny-yolo_v2-coco
181
40
28.7
10.5
80 Doc
GitHub

Tips for selecting a model

Pretrained object detectors have different characteristics that matter when choosing a network to apply to your problem. The most important characteristics are mean average precision (mAP), speed, and size. Choosing a network is generally a tradeoff between these characteristics.

Application Specific Object Detectors

These networks have been trained to detect specific objects for a given application.

Network Application Size (MB) Location Example Output
Spatial-CNN Lane detection 74 GitHub
RESA Road Boundary detection 95 GitHub
Single Shot Detector (SSD) Vehicle detection 44 Doc
Faster R-CNN Vehicle detection 118 Doc

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Semantic Segmentation

Segmentation is essential for image analysis tasks. Semantic segmentation describes the process of associating each pixel of an image with a class label, (such as flower, person, road, sky, ocean, or car).

Inputs are RGB images, outputs are pixel classifications (semantic maps).

This network has been trained to detect 20 objects classes from the PASCAL VOC dataset:

Network Size (MB) Mean Accuracy Object Classes Location
DeepLabv3+ 209 0.87 20 GitHub

Zero-shot image segmentation model:

Network Size (MB) Example Location
segmentAnythingModel 358 Doc

Application Specific Semantic Segmentation Models

Network Application Size (MB) Location Example Output
U-net Raw Camera Processing 31 Doc
3-D U-net Brain Tumor Segmentation 56.2 Doc
AdaptSeg (GAN) Model tuning using 3-D simulation data 54.4 Doc

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Instance Segmentation

Instance segmentation is an enhanced type of object detection that generates a segmentation map for each detected instance of an object. Instance segmentation treats individual objects as distinct entities, regardless of the class of the objects. In contrast, semantic segmentation considers all objects of the same class as belonging to a single entity.

Inputs are RGB images, outputs are pixel classifications (semantic maps), bounding boxes and classification labels.

Network Object Classes Location
Mask R-CNN 80 Doc
Github

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Image Translation

Image translation is the task of transferring styles and characteristics from one image domain to another. This technique can be extended to other image-to-image learning operations, such as image enhancement, image colorization, defect generation, and medical image analysis.

Inputs are images, outputs are translated RGB images. This example workflow shows how a semantic segmentation map input translates to a synthetic image via a pretrained model (Pix2PixHD):

Network Application Size (MB) Location Example Output
Pix2PixHD(CGAN) Synthetic Image Translation 648 Doc
UNIT (GAN) Day-to-Dusk Dusk-to-Day Image Translation 72.5 Doc
UNIT (GAN) Medical Image Denoising 72.4 Doc
CycleGAN Medical Image Denoising 75.3 Doc
VDSR Super Resolution (estimate a high-resolution image from a low-resolution image) 2.4 Doc

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Pose Estimation

Pose estimation is a computer vision technique for localizing the position and orientation of an object using a fixed set of keypoints.

All inputs are RGB images, outputs are heatmaps and part affinity fields (PAFs) which via post processing perform pose estimation.

Network Backbone Networks Size (MB) Location
OpenPose vgg19 14 Doc
HR Net human-full-body-w32
human-full-body-w48
106.9
237.7
Doc

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3D Reconstruction

3D reconstruction is the process of capturing the shape and appearance of real objects.

Network Size (MB) Location Example Output
NeRF 3.78 GitHub NeRF

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Video Classification

Video classification is a computer vision technique for classifying the action or content in a sequence of video frames.

All inputs are Videos only or Video with Optical Flow data, outputs are gesture classifications and scores.

Network Inputs Size(MB) Classifications (Human Actions) Description Location
SlowFast Video 124 400 Faster convergence than Inflated-3D Doc
R(2+1)D Video 112 400 Faster convergence than Inflated-3D Doc
Inflated-3D Video & Optical Flow data 91 400 Accuracy of the classifier improves when combining optical flow and RGB data. Doc

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Text Detection and Recognition

Text detection is a computer vision technique used for locating instances of text within in images.

Inputs are RGB images, outputs are bounding boxes that identify regions of text.

Network Application Size (MB) Location
CRAFT Trained to detect English, Korean, Italian, French, Arabic, German and Bangla (Indian). 3.8 Doc
GitHub

Application Specific Text Detectors

Network Application Size (MB) Location Example Output
Seven Segment Digit Recognition Seven segment digit recognition using deep learning and OCR. This is helpful in industrial automation applications where digital displays are often surrounded with complex background. 3.8 Doc
GitHub

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Transformers (Text)

Transformer pretained models have already learned to extract powerful and informative features features from text. Use them as a starting point to learn a new task using transfer learning.

Inputs are sequences of text, outputs are text feature embeddings.

Network Applications Size (MB) Location
BERT Feature Extraction (Sentence and Word embedding), Text Classification, Token Classification, Masked Language Modeling, Question Answering 390 GitHub
Doc
all-MiniLM-L6-v2 Document Embedding, Clustering, Information Retrieval 80 Doc
all-MiniLM-L12-v2 Document Embedding, Clustering, Information Retrieval 120 Doc

Application Specific Transformers

Network Application Size (MB) Location Output Example
FinBERT The FinBERT model is a BERT model for financial sentiment analysis 388 GitHub
GPT-2 The GPT-2 model is a decoder model used for text summarization. 1.2GB GitHub

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Audio Embeddings

Audio embedding pretrained models have already learned to extract powerful and informative features from audio signals. Use them as a starting point to learn a new task using transfer learning.

Inputs are audio signals, outputs are audio feature embeddings.

Note 2: Since R2024a, please use the audioPretrainedNetwork function instead and specify the pretrained model. For example, use the following code to access VGGish:

net = audioPretrainedNetwork("vggish");
Network Application Size (MB) Location
VGGish2 Feature Embeddings 257 Doc
OpenL32 Feature Embeddings 200 Doc

Application Specific Audio Models

Network Application Size (MB) Output Classes Location Output Example
vadnet2 Voice Activity Detection (regression) 0.427 - Doc
YAMNet2 Sound Classification 13.5 521 Doc
CREPE2 Pitch Estimation (regression) 132 - Doc

Speech to Text

Speech-to-text models provide a fast, efficient method to convert spoken language into written text, enhancing accessibility for individuals with disabilities, enabling downstream tasks like text summarization and sentiment analysis, and streamlining documentation processes. As a key element of human-machine interfaces, including personal assistants, it allows for natural and intuitive interactions, enabling machines to understand and execute spoken commands, improving usability and broadening inclusivity across various applications.

Inputs are audio signals, outputs is text.

Network Application Size (MB) Word Error Rate (WER) Location
wav2vec Speech to Text 236 3.2 GitHub
deepspeech Speech to Text 167 5.97 GitHub

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Lidar

Point cloud data is acquired by a variety of sensors, such as lidar, radar, and depth cameras. Training robust classifiers with point cloud data is challenging because of the sparsity of data per object, object occlusions, and sensor noise. Deep learning techniques have been shown to address many of these challenges by learning robust feature representations directly from point cloud data.

Inputs are Lidar Point Clouds converted to five-channels, outputs are segmentation, classification or object detection results overlayed on point clouds.

Network Application Size (MB) Object Classes Location
PointNet Classification 5 14 Doc
PointNet++ Segmentation 3 8 Doc
PointSeg Segmentation 14 3 Doc
SqueezeSegV2 Segmentation 5 12 Doc
SalsaNext Segmentation 20.9 13 GitHub
PointPillars Object Detection 8 3 Doc
Complex YOLO v4 Object Detection 233 (complex-yolov4)
21 (tiny-complex-yolov4)
3 GitHub

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Manipulator Motion Planning

Manipulator motion planning is a technique used to plan a trajectory for a robotic arm from a start position to a goal position in an obstacle environment.

Pretrained deep learning models have learned to plan such trajectories for repetitive tasks such as picking and placing of objects, leading to speed ups over traditional algorithms.

Inputs are start configuration, goal configuration and obstacle environment encoding for the robot, outputs are intermediate trajectory guesses.

Network Application Size (MB) Location
Deep-Learning-Based CHOMP (DLCHOMP) Trajectory Prediction 25 Doc
GitHub

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Path Planning with Motion Planning Networks

Motion Planning Networks (MPNet) is a deep-learning-based approach for finding optimal paths between a start point and goal point in motion planning problems. MPNet is a deep neural network that can be trained on multiple environments to learn optimal paths between various states in the environments. The MPNet uses this prior knowledge to,

  • Generate informed samples between two states in an unknown test environment. These samples can be used with sampling-based motion planners such as optimal rapidly-exploring random trees (RRT*) for path planning.
  • Compute collision-free path between two states in an unknown test environment. MPNet based path planner is more efficient than the classical path planners such as the RRT*.

To know more please visit Get Started with Motion Planning Networks

Network Application Size (MB) Location
mazeMapTrainedMPNET Path Planning 0.23 Doc

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Model requests

If you'd like to request MATLAB support for additional pretrained models, please create an issue from this repo.

Alternatively send the request through to:

Jianghao Wang
Deep Learning Product Manager
[email protected]

Copyright 2024, The MathWorks, Inc.