Does anyone use TX2 to implement deeplab v3 semantic segmentation? Is there any sample code for this?
Hi,
Sorry that we don’t have too much experience on the deeplab V3 model.
<img src="https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/deep-vision-header.jpg" width="100%">
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<sup>Semantic Segmentation</sup></s></p>
# Semantic Segmentation with SegNet
The next deep learning capability we'll cover in this tutorial is **semantic segmentation**. Semantic segmentation is based on image recognition, except the classifications occur at the pixel level as opposed to the entire image. This is accomplished by *convolutionalizing* a pre-trained image recognition backbone, which transforms the model into a [Fully Convolutional Network (FCN)](https://arxiv.org/abs/1605.06211) capable of per-pixel labeling. Especially useful for environmental perception, segmentation yields dense per-pixel classifications of many different potential objects per scene, including scene foregrounds and backgrounds.
<img src="https://github.com/dusty-nv/jetson-inference/raw/pytorch/docs/images/segmentation.jpg">
[`segNet`](../c/segNet.h) accepts as input the 2D image, and outputs a second image with the per-pixel classification mask overlay. Each pixel of the mask corresponds to the class of object that was classified. [`segNet`](../c/segNet.h) is available to use from [Python](https://rawgit.com/dusty-nv/jetson-inference/pytorch/docs/html/python/jetson.inference.html#segNet) and [C++](../c/segNet.h).
As examples of using the `segNet` class, we provide sample programs C++ and Python:
- [`segnet.cpp`](../examples/segnet/segnet.cpp) (C++)
- [`segnet.py`](../python/examples/segnet.py) (Python)
These samples are able to segment images, videos, and camera feeds. For more info about the various types of input/output streams supported, see the [Camera Streaming and Multimedia](aux-streaming.md) page.
See [below](#pretrained-segmentation-models-available) for various pre-trained segmentation models available that use the FCN-ResNet18 network with realtime performance on Jetson. Models are provided for a variety of environments and subject matter, including urban cities, off-road trails, and indoor office spaces and homes.
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Thanks.
@AastaLLL thanks your reply. I can check semantic segmentation sample code in jetson-inference, also will try if we can port deeplab v3 model to Jetson/TX2 and run it with TensorRT.
Hi,
To port deeplab V3 on Jetson, you can get TensorFlow installed with below instructions:
Thanks.