MATLAB的预训练DeepLabv3+语义分割模型.zip

# Pretrained DeepLabv3+ Network for Semantic Segmentation This repository provides a pretrained DeepLabv3+[1] semantic segmentation model for MATLAB®. Requirements ------------ - MATLAB® R2020a or later. - Deep Learning Toolbox™. - Computer Vision Toolbox™. Overview -------- Semantic segmentation is a computer vision technique for segmenting different classes of objects in images or videos. This pretrained network is trained using PASCAL VOC dataset[2] which have 20 different classes including airplane, bus, car, train, person, horse etc. For more information about semantic segmentation, see [Getting Started with Semantic Segmentation Using Deep Learning](https://mathworks.com/help/vision/ug/getting-started-with-semantic-segmentation-using-deep-learning.html). Getting Started --------------- Download or clone this repository to your machine and open it in MATLAB®. ### Download the pretrained network Use the below helper to download the pretrained network. ``` model = helper.downloadPretrainedDeepLabv3Plus; net = model.net; ``` Semantic Segmentation Using Pretrained DeepLabv3+ ------------------------------------------------- ``` % Read test image from images folder image = imread('visionteam.jpg'); % Resize the image to the size used to train the network. % The image is resized such that smallest dimension is 513. sz = size(image); [~,k] = min(sz(1:2)); scale = 513/sz(k); img = imresize(image, scale, "bilinear"); % Use semanticseg function to generate segmentation map. result = semanticseg(img, net); % Generate the overlaid result using generated map. overlay = labeloverlay(img , result, 'Transparency', 0.4); % Visualize the input and the result. overlay = imresize(overlay, sz(1:2), 'bilinear'); montage({image, overlay}); ``` Left-side image is the input and right-side image is the corresponding segmentation output.  Train Custom DeepLabv3+ Using Transfer Learning ----------------------------------------------- Transfer learning enables you to adapt a pretrained DeepLabv3+ network to your dataset. Create a custom DeepLabv3+ network for transfer learning with a new set of classes using the `configureDeepLabv3PlusTransferLearn.m` script. For more information about training a DeepLabv3+ network, see [Semantic Segmentation Using Deep Learning](https://www.mathworks.com/help/vision/ug/semantic-segmentation-using-deep-learning.html) Code Generation for DeepLabV3+ ------------------------------ Code generation enables you to generate code and deploy DeepLabv3+ on multiple embedded platforms. Run `codegenDeepLabv3Plus.m`. This script calls the `deepLabv3Plus_predict.m` entry point function and generate CUDA code for it. It will run the generated MEX and gives output. | Model | Inference Speed (FPS) | | ------ | ------ | | DeepLabv3Plus w/o codegen | 3.5265 | | DeepLabv3Plus with codegen | 21.5526 | - Performance (in FPS) is measured on a TITAN-RTX GPU using 513x513 image. For more information about codegen, see [Deep Learning with GPU Coder](https://www.mathworks.com/help/gpucoder/gpucoder-deep-learning.html) Accuracy -------- Metrics are mIoU, global accuracy and mean accuracy computed over 2012 PASCAL VOC val data. | Model | mIoU | Global Accuracy | Mean Accuracy | Size (MB) | Classes | | ------ | ------ | ------ | ------ | ------ | ------ | | DeepLabv3Plus-VOC | 0.77299 | 0.94146 | 0.87279 | 209 | [voc class names](+helper/pascal-voc-classes.txt) | - During computation of these metrics, val images are first resized such that the smaller dimension of the images are scaled to 513 because that matches the training preprocessing and then a center crop of size 513x513 is used for evaluation. References ----------- [1] Chen, Liang-Chieh, et al. "Encoder-decoder with atrous separable convolution for semantic image segmentation." Proceedings of the European conference on computer vision (ECCV). 2018. [2] The PASCAL Visual Object Classes Challenge: A Retrospective Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J. and Zisserman, A. International Journal of Computer Vision, 111(1), 98-136, 2015. Copyright 2021 The MathWorks, Inc.