Details about the network architecture can be found in the following arXiv paper:. Tran, Du, et al. Download: weights. Hi mzolfaghariActually I don't know any tool to do that. If you want to give a try, there is a repo that ports from Caffe to Keras, so digging into the code, you may find the way to do it on the inverse way. I downloaded your code and tried to run it.
It seems that the downloaded file caffe. Defaulted to proto2 syntax. I wonder whether there is any difference between them? Hi Alberto, Great job! Thanks for sharing!
How do you know that ZeroPadding3D zeropadding3d None,2, 9, 9 0 layer is used? I was not able to find it in the original Caffe model Could you please point the place where it is?
Can you share your code about converting sport1m caffe model to its tensorflow version? Do you know on which of them conv3D was trained? Could you please tell are there any problems with. Could you please help me. Thank you so much!
Could you specify the version of your python, your Keras, and Theano? I am running it here on June 14 Now the latest version cannot be compatible with the old one, so when I read your model json file and h5 file, it shows error. It also shows error when I define the model structure by myself and load h5 file. I have a problem loading the weights in my model. I thought it might be because I use tensorflow backend for my keras, so I decided to convert the weights, but I still get the same error:.
ValueError: You are trying to load a weight file containing 0 layers into a model with 11 layers. Hi, The links on weights and model to keras model you gave have some issues. Or my versions should be missmatched. Here are my libraries and versions. When using previously created model by adding layers manually to lead h5 file weights here is the error ValueError: You are trying to load a weight file containing 0 layers into a model with 11 layers.
The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information.
It relies on being able to grab the diagonal of the matrix and reshape it. From the website above:. Can this be done in Tensorflow? This seems like a relatively easy task that can still use the vast majority of Tensorflow's CNN framework while handling 3D datatypes. Learn more. Asked 4 years, 2 months ago. Active 2 years, 2 months ago. Viewed times. Gavin Gray 3 3 silver badges 5 5 bronze badges.
Here's an example of extracting two diagonals from a matrix, you could generalize it to extract n diagonals and stack them together: stackoverflow. YaroslavBulatov - could you put that comment into an answer so the question is 'answered'? Active Oldest Votes. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog. The Overflow How many jobs can be done at home?
Socializing with co-workers while social distancing. Featured on Meta. Community and Moderator guidelines for escalating issues via new response….
Feedback on Q2 Community Roadmap. Triage needs to be fixed urgently, and users need to be notified upon…. Technical site integration observational experiment live on Stack Overflow. Dark Mode Beta - help us root out low-contrast and un-converted bits. Linked 9. Related 4.
Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information.
I am fairly new to neural networks and I am planning to train a network using 3D data 3D face scans in. With my idea that would require placing my 3D points on a 2D grid so that the input would be just like with images but with XYZ coordinates instead of RGB. Is it a sensible idea that could work with CNN? If so, what is the best way to build that 2D projection?
Yes, but doing that I would lose a lot of information wouldn't I? I thought that points that are not visible from frontal view could be also placed in the 2D grid and my input would be in form Xyz for each "pixel". How about using a frontal RGB image interleaved with an additional frontal depth texture, found by rendering the depth from the front or from original scanner data, if it was a frontal scan?
Learn more. Ask Question. Asked 2 years, 5 months ago. Active 2 years, 2 months ago. Viewed times. I do not know what is your final target but this guys did a great job in a similar problem recently: blog.
Thank you for the link, it's a very interesting reading and I might reconsider my initial idea of doing the 2D projection. Though I am still curious whether my approach might work to and if so then how to do it correctly.
For doing that, a simple 2D image would be enough. So I think that you would like to transform your 3D image in a frontal image of the person? Is that correct? Just use a 3D CNN?. Active Oldest Votes. Yes, you would lose a lot of information. But you would simplify the problem a lot too and you want to figure out the age of a person, so you should keep things simple for the NN regression.
If I were you I would proceed stepwise, try with something simple and keep adding complexity to it only if necessary. No, take the 3 channels RGB if you can, you need to find a transformation to convert your image to 2D with 3 channels.Visualizing Convolutional Neural Networks using Lucid (AI Adventures)
I think you need get more insights about how do CNN work, take a look at this tutorial I always recommend it for people that know Neural Nets and want to start with Conv. Elias Hasle Elias Hasle 2 2 silver badges 11 11 bronze badges. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown.Abstract Polygonal meshes provide an efficient representation for 3D shapes.
They explicitly capture both shape surface and topology, and leverage non-uniformity to represent large flat regions as well as sharp, intricate features. This non-uniformity and irregularity, however, inhibits mesh analysis efforts using neural networks that combine convolution and pooling operations.
In this paper, we utilize the unique properties of the mesh for a direct analysis of 3D shapes using MeshCNN, a convolutional neural network designed specifically for triangular meshes. Analogous to classic CNNs, MeshCNN combines specialized convolution and pooling layers that operate on the mesh edges, by leveraging their intrinsic geodesic connections. Convolutions are applied on edges and the four edges of their incident triangles, and pooling is applied via an edge collapse operation that retains surface topology, thereby, generating new mesh connectivity for the subsequent convolutions.
MeshCNN learns which edges to collapse, thus forming a task-driven process where the network exposes and expands the important features while discarding the redundant ones. We demonstrate the effectiveness of our task-driven pooling on various learning tasks applied to 3D meshes. The input edge feature is a 5-dimensional vector every edge: the dihedral angle, two inner angles and two edge-length ratios for each face.
Input Edge Features. Mesh Convolution. Results Learned Simplifications on Cube Dataset. Learned Simplifications on Shrec Dataset.
Human Segmentation Results. Coseg Segmentation Results.Any idea about that? Thank you. This repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study".
This repository contains the code of HyperDenseNet, a hyper-densely connected CNN to segment medical images in multi-modal image scenarios. Currently we only have documentation based on docstrings. Pytorch version of the HyperDenseNet deep neural network for multi-modal image segmentation. PyTorch code for Class Visualization Pyramid for intpreting spatio-temporal class-specific activations throughout the network.
We tried to implement a system for per-Node and whole-Model performance profiling by measuring local i. Related code:. My first repo on GitHub, a portfolio of some data science projects I undertook. Add a description, image, and links to the 3d-cnn topic page so that developers can more easily learn about it. Curate this topic.
To associate your repository with the 3d-cnn topic, visit your repo's landing page and select "manage topics. Learn more. Skip to content. Here are 24 public repositories matching this topic Language: All Filter by language. Sort options. Star Code Issues Pull requests. OneManArmy93 commented Jul 24, Thank you Read more.
Updated Nov 20, Python. Tutorial about 3D convolutional network. Updated Oct 30, Python. Updated Mar 25, Jupyter Notebook. Updated Oct 28, Python. Open High-level documentation. Read more. Updated Apr 2, Python. Updated May 18, Python.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. The link to the paper is provided as well. The code has been developed using TensorFlow.
The input pipeline must be prepared by the users. This code is aimed to provide the implementation for Speaker Verification SR by using 3D convolutional neural networks following the SR protocol.
Convolutional Neural Network (CNN)
We leveraged 3D convolutional architecture for creating the speaker model in order to simultaneously capturing the speech-related and temporal information from the speakers' utterances. At the development phasea CNN is trained to classify speakers at the utterance-level. In the enrollment stagethe trained network is utilized to directly create a speaker model for each speaker based on the extracted features.
Finally, in the evaluation phasethe extracted features from the test utterance will be compared to the stored speaker model to verify the claimed identity. The aforementioned three phases are usually considered as the SV protocol. One of the main challenges is the creation of the speaker models. Previously-reported approaches create speaker models based on averaging the extracted features from utterances of the speaker, which is known as the d-vector system.
In our paper, we propose the implementation of 3D-CNNs for direct speaker model creation in which, for both development and enrollment phases, an identical number of speaker utterances is fed to the network for representing the spoken utterances and creation of the speaker model.
This leads to simultaneously capturing the speaker-related information and building a more robust system to cope with within-speaker variation. We demonstrate that the proposed method significantly outperforms the d-vector verification system. The input pipeline must be provided by the user. The MFCC features can be used as the data representation of the spoken utterances at the frame level. This operation disturbs the locality property and is in contrast with the local characteristics of the convolutional operations.
The employed approach in this work is to use the log-energies, which we call MFECs. The temporal features are overlapping 20ms windows with the stride of 10ms, which are used for the generation of spectrum features.
From a 0. The speech features have been extracted using [SpeechPy] package. The Slim high-level API made our life very easy. The following script has been used for our implementation:. As it can be seen, slim. The base of the slim.
Please refer to official Documentation for further details. The code architecture part has been heavily inspired by Slim and Slim image classification library. Please refer to this link for further details.Hi akors. I was wondering if you had the rest of the code that you used to make this run.
I'm trying to adapt this into a demo 3D CNN that will classify weather there is a sphere or a cube in a set of synthetic 3D images I made.
Specifically, I'm wondering what trainer you used and how to connect the inference and loss to the trainer and run it on a 4D matrix containing the 3D images and an array of labels.
Hi, this is very nice code for understanding 3D convolution. Skip to content. Instantly share code, notes, and snippets. Code Revisions 1 Stars 4 Forks Embed What would you like to do?
Embed Embed this gist in your website. Share Copy sharable link for this gist. Learn more about clone URLs. Download ZIP. Example of 3D convolutional network with TensorFlow. This comment has been minimized. Sign in to view.
Copy link Quote reply. Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.