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:

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.

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.

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
Single Shot Detector (SSD)	Vehicle detection	44	Doc
Faster R-CNN	Vehicle detection	118	Doc

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:

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

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.

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

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.

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.

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.

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	GitHub

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.

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

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.

Application Specific Audio Models

Network	Application	Size (MB)	Output Classes	Location	Output Example
YAMNet	Sound Classification	13.5	521	Doc
CREPE	Pitch Estimation (Regression)	132	-	Doc

Speech to text pretrained models take an audio input and translate it into a text output. They are useful in digitizating audio files for downstream text processing tasks such as text summarization and sentiment analysis.

Inputs are Audio signals, outputs is text.

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.