MultiNet | Real-time Joint Semantic Reasoning for Autonomous Driving | Machine Learning library

by MarvinTeichmann Python Version: Current License: MIT

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kandi X-RAY | MultiNet Summary

MultiNet is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Keras applications. MultiNet has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

MultiNet is able to jointly perform road segmentation, car detection and street classification. The model achieves real-time speed and state-of-the-art performance in segmentation. Check out our paper for a detailed model description. MultiNet is optimized to perform well at a real-time speed. It has two components: KittiSeg, which sets a new state-of-the art in road segmentation; and KittiBox, which improves over the baseline Faster-RCNN in both inference speed and detection performance. The model is designed as an encoder-decoder architecture. It utilizes one VGG encoder and several independent decoders for each task. This repository contains generic code that combines several tensorflow models in one network. The code for the individual tasks is provided by the KittiSeg, KittiBox, and KittiClass repositories. These repositories are utilized as submodules in this project. This project is built to be compatible with the TensorVision back end, which allows for organizing experiments in a very clean way.

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Support

MultiNet has a low active ecosystem.

It has 521 star(s) with 251 fork(s). There are 47 watchers for this library.

It had no major release in the last 6 months.

There are 31 open issues and 23 have been closed. On average issues are closed in 15 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of MultiNet is current.

Quality

MultiNet has 0 bugs and 0 code smells.

Security

MultiNet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

MultiNet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

MultiNet is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

MultiNet releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

MultiNet saves you 453 person hours of effort in developing the same functionality from scratch.

It has 1071 lines of code, 32 functions and 4 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed MultiNet and discovered the below as its top functions. This is intended to give you an instant insight into MultiNet implemented functionality, and help decide if they suit your requirements.

Runs united training
Print training status
Builds the union of two models
Build the training graph
Combine 3 losses
Combine two losses
Loads the union of two models
Evaluate a single image
Evaluate the runtime
Draws an image
Test whether segmentation input is resized
Generate output from tensors
Test if two tasks have constant input
Process an image
Test if segmentation input is not resized
Get data and run directory
Download and extract weights
Test if the constant input is valid
Download a file

Get all kandi verified functions for this library.

MultiNet Key Features

No Key Features are available at this moment for MultiNet.

MultiNet Examples and Code Snippets

No Code Snippets are available at this moment for MultiNet.

Community Discussions

Trending Discussions on MultiNet

Tensorflow quantization

QUESTION

Tensorflow quantization

Asked 2018-Feb-09 at 06:58

I would like to optimize a graph using Tensorflow's transform_graph tool. I tried optimizing the graph from MultiNet (and others with similar encoder-decoder architectures). However, the optimized graph is actually slower when using quantize_weights, and even much slower when using quantize_nodes. From Tensorflow's documentation, there may be no improvements, or it may even be slower, when quantizing. Any idea if this is normal with the graph/software/hardware below?

Here is my system information for your reference:

OS Platform and Distribution: Linux Ubuntu 16.04
TensorFlow installed from: using TF source code (CPU) for graph conversion, using binary-python(GPU) for inference
TensorFlow version: both using r1.3
Python version: 2.7
Bazel version: 0.6.1
CUDA/cuDNN version: 8.0/6.0 (inference only)
GPU model and memory: GeForce GTX 1080 Ti

I can post all the scripts used to reproduce if necessary.

...

ANSWER

Answered 2017-Oct-25 at 10:10

It seems like quantization in Tensorflow only happens on CPUs. See: https://github.com/tensorflow/tensorflow/issues/2807

Source https://stackoverflow.com/questions/46662239

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install MultiNet

Running the model using demo.py only requires you to perform step 1-3. Step 4 and 5 is only required if you want to train your own model using train.py. Note that I recommend using download_data.py instead of downloading the data yourself. The script will also extract and prepare the data. See Section Manage data storage if you like to control where the data is stored. If you forget the second step you might end up with an inconstant repository state. You will already have the new code for MultiNet but run it old submodule versions code. This can work, but I do not run any tests to verify this.
Clone this repository: https://github.com/MarvinTeichmann/MultiNet.git
Initialize all submodules: git submodule update --init --recursive
cd submodules/KittiBox/submodules/utils/ && make to build cython code
[Optional] Download Kitti Road Data: Retrieve kitti data url here: http://www.cvlibs.net/download.php?file=data_road.zip Call python download_data.py --kitti_url URL_YOU_RETRIEVED
[Optional] Run cd submodules/KittiBox/submodules/KittiObjective2/ && make to build the Kitti evaluation code (see submodules/KittiBox/submodules/KittiObjective2/README.md for more information)
Pull all patches: git pull
Update all submodules: git submodule update --init --recursive
Run: python demo.py --gpus 0 --input data/demo/um_000005.png to obtain a prediction using demo.png as input. Run: python evaluate.py to evaluate a trained model. Run: python train.py --hypes hypes/multinet2.json to train a multinet2. If you like to understand the code, I would recommend looking at demo.py first. I have documented each step as thoroughly as possible in this file. Only training of MultiNet3 (joint detection and segmentation) is supported out of the box. The data to train the classification model is not public an those cannot be used to train the full MultiNet3 (detection, segmentation and classification). The full code is given here, so you can still train MultiNet3 if you have your own data.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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