MATLAB Deep Learning Model Hub

Discover pretrained models for deep learning in MATLAB.

Models

Computer Vision

• Image Classification
• Object Detection
• Semantic Segmentation
• Instance Segmentation
• Image Translation
• Pose Estimation
• 3D Reconstruction
• Video Classification
• Text Detection & Recognition

Natural Language Processing

• Transformers

Audio

• Audio Embeddings
• Sound Classification
• Pitch Estimation
• Speech to Text

Lidar

• Point Cloud Classification
• Point Cloud Segmentation
• Point Cloud Object Detection

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.

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 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

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

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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.

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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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 2023, The MathWorks, Inc.