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For ML6, the technical impact has been extremely limited as long as our
employees have a stable internet connection at home and the projects we are
developing is done on Google Cloud or any other public cloud.
The main reason everything keeps working is the lack of a central VPN server
since our internal applications and the managed Google services are protected
by an Identity Aware Proxy.
Let’s dive in a bit of theory why this works and it is more secure than
centrally managed VPN solutions.
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### Zero Trust Architecture
In traditional security architecture, every employee who wants to access
applications or other services on a corporate network needs to be on the
internal network. If you are not close to a local network, you have to join
the network using a VPN.
The VPN and various firewalls are the perimeters you have to pass to get
access. As soon as you are on the internal network, certain applications are
protected by another layer of security but often network
drives/databases/servers are accessible from a technical point of view.
If a hacker can get through the firewall or connect to your internal network
you might have a security breach. | scraping/output/-5981754466922191164.txt | [
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Zero Trust is a security concept centered on the belief that organizations
should not automatically trust anything inside or outside its perimeters.
Every call to an application or service on the network should be verified
before granting access. This boils down to 2 questions. Do we grant the user
access? And do we trust the device?
This concept enforces more fine-grained control on application level and at
the same time reduces the number of perimeters to manage.
Firewalls and networking routing is still essential but the more difficult to
scale VPN layer can be removed for a number of applications.
This ensures that you can work safely without the need for any VPN or remote
desktop client. In this scenario, it’s a no-brainer to enable MFA, multi-
factor authentication for every user. MFA, preferably using hardware keys, is
another modern layer of security to avoid unauthorised access. | scraping/output/-5981754466922191164.txt | [
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Reference:https://www.microsoft.com/en-us/microsoft-365/blog/2019/09/18/why-
banks-adopt-modern-cybersecurity-zero-trust-model/
### Zero Trust Architecture on Google Cloud Platform
Google, with the BeyondCorp methodology, is a forerunner in zero trust
architecture. Enabling zero trust security for web applications is straight
forward using the managed Identify Aware Proxy service if your application
runs on Google Cloud Platform.
First of all, you need to manage your users and devices using Cloud Identity,
then follow the scenarios outlines in the Identity Aware Proxy documentation
based on how your application has been deployed.
IAP for apps deployed on Google Kubernetes Engine | scraping/output/-5981754466922191164.txt | [
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IAP for apps deployed on Google Kubernetes Engine
If you want to protect applications running on your on-premise network it’s
required to setup a Cloud Interconnect or Cloud VPN to route traffic between
the networks in a secure way. Compared to on-premise VPN solutions these
services are easy to scale up and down and don’t require long-term license
agreements.
IAP for apps deployed on-premise
If you are using AD, Active Directory, to manage your users it’s easy to
provision these users in Cloud Identity using the Google Cloud Directory Sync
service.
Check out the VPC Service Control service to reduce the risk of data
exfiltration.
### Conclusion
So let’s try to turn the coronavirus into a driver for innovation!
Check if zero trust security is a short-term or long-term solution to reduce
the impact and costs of VPN on your organization. | scraping/output/-5981754466922191164.txt | [
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So let’s try to turn the coronavirus into a driver for innovation!
Check if zero trust security is a short-term or long-term solution to reduce
the impact and costs of VPN on your organization.
We’ve added plenty of references to Google, open-source and other public cloud
vendors zero trust solutions so happy reading and experimenting.
References about zero trust architecture
-https://www.tijd.be/dossiers/coronavirus/thuiswerk-is-hindernisrace-voor-bedrijven/10214784.html
-https://en.wikipedia.org/wiki/Zero_Trust
-https://www.csoonline.com/article/3247848/what-is-zero-trust-a-model-for-more-effective-security.html
-https://www.cloudflare.com/learning/security/glossary/what-is-zero-trust/ | scraping/output/-5981754466922191164.txt | [
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Zero trust on Google Cloud Platform
-https://medium.com/google-cloud/what-is-beyondcorp-what-is-identity-aware-proxy-de525d9b3f90
-https://cloud.google.com/beyondcorp/
-https://www.blog.google/products/google-cloud/how-use-beyondcorp-ditch-your-vpn-improve-security-and-go-cloud/
-https://www.blog.google/products/google-cloud/preparing-for-a-beyondcorp-world-at-your-company/
-https://cloud.google.com/beyondcorp/#researchPapers | scraping/output/-5981754466922191164.txt | [
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] |
Zero trust @ public clouds & other open-source solutions
-https://zero.pritunl.com/
-https://www.microsoft.com/en-us/security/business/zero-trust
-https://www.microsoft.com/en-us/microsoft-365/blog/2019/09/18/why-banks-adopt-modern-cybersecurity-zero-trust-model/
-https://aws.amazon.com/blogs/publicsector/how-to-think-about-zero-trust-architectures-on-aws/
-https://teams.cloudflare.com/access/index.html
-https://www.hashicorp.com/resources/how-zero-trust-networking
-https://www.akamai.com/us/en/solutions/security/zero-trust-security-model.jsp
### About ML6
We are a team of ML experts composing the fastest growing AI company in
Belgium. With offices in Ghent, Amsterdam, Berlin and London, we implement
self learning systems across different sectors to improve and optimise our
clients operations. We do this by staying on top of research, innovation and
practical applications. Find out more at www.ml6.eu
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March 10, 2023
# Who spoke when: Choosing the right speaker diarization tool
Contributors
Philippe Moussali
Machine Learning Engineer
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Thank you! Your submission has been received! | scraping/output/5364586834657158796.txt | [
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### Introduction
This blogpost is derived from its interactive version on Hugging Face Spaces.
You can continue reading there if you want the benefits of playing around with
multiple examples or to test some diarization tools on your own audio samples.
Illustration of speaker diarization
With the increase in applications of automated speech recognition systems
(ASR), the ability to partition a speech audio stream with multiple speakers
into individual segments associated with each individual has become a crucial
part of understanding speech data.
In this blog post, we will take a look at different open source frameworks for
speaker diarization and provide you with a guide to pick the most suited one
for your use case. | scraping/output/5364586834657158796.txt | [
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In this blog post, we will take a look at different open source frameworks for
speaker diarization and provide you with a guide to pick the most suited one
for your use case.
Before we get into the technical details, libraries and tools, let’s first
understand what speaker diarization is and how it works!
### What is speaker diarization?️
Speaker diarization aims to answer the question of “who spoke when”. In short:
diariziation algorithms break down an audio stream of multiple speakers into
segments corresponding to the individual speakers.
By combining the information that we get from diarization with ASR
transcriptions, we can transform the generated transcript into a format which
is more readable and interpretable for humans and that can be used for other
downstream NLP tasks.
Workflow of combining the output of both ASR and speaker diarization on a
speech signal to generate a speaker transcript.
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Workflow of combining the output of both ASR and speaker diarization on a
speech signal to generate a speaker transcript.
Let’s illustrate this with an example. We have a recording of a casual phone
conversation between two people. You can see what the different transcriptions
look like when we transcribe the conversation with and without diarization.
By generating a speaker-aware transcript, we can more easily interpret the
generated conversation compared to a generated transcript without diarization.
Much neater no?
But what can I do with those speaker-aware transcripts?
Speaker-aware transcripts can be a powerful tool for analyzing speech data: | scraping/output/5364586834657158796.txt | [
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But what can I do with those speaker-aware transcripts?
Speaker-aware transcripts can be a powerful tool for analyzing speech data:
* We can use the transcripts to analyze individual speaker’s sentiment by using sentiment analysis on both audio and text transcripts.
* Another use case is telemedicine, where we might identify the <doctor> and <patient> tags on the transcription to create an accurate transcript and attach it to the patient file or EHR system.
* Speaker diarization can be used by hiring platforms to analyze phone and video recruitment calls. This allows them to split and categorize candidates depending on their response to certain questions without having to listen to the recordings again.
Now that we’ve seen the importance of speaker diarization and some of its
applications, it’s time to find out how we can implement diarization workflows
on our audio data.
### The workflow of a speaker diarization system | scraping/output/5364586834657158796.txt | [
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### The workflow of a speaker diarization system
Building robust and accurate speaker diarization workflows is not a trivial
task. Real world audio data is messy and complex due to many factors, such as
having a noisy background, multiple speakers talking at the same time and
subtle differences between the speakers’ voices in pitch and tone.
Moreover, speaker diarization systems often suffer from domain mismatch where
a model on data from a specific domain works poorly when applied to another
domain.
All in all, tackling speaker diarization is no easy feat. Current speaker
diarization systems can be divided into two categories: Traditional systems
and End-to-End systems. Let’s look at how they work:
### Traditional diarization systems
Those consist of many independent submodules that are optimized individually,
namely being: | scraping/output/5364586834657158796.txt | [
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* Speech detection: The first step is to identify speech and remove non-speech signals with a voice activity detector (VAD) algorithm.
* Speech segmentation: The output of the VAD is then segmented into small segments consisting of a few seconds (usually 1–2 seconds).
* Speech embedder: A neural network pre-trained on speaker recognition is used to derive a high-level representation of the speech segments. Those embeddings are vector representations that summarize the voice characteristics (a.k.a voice print).
* Clustering: After extracting segment embeddings, we need to cluster the speech embeddings with a clustering algorithm (for example K-Means or spectral clustering). The clustering produces our desired diarization results, which consists of identifying the number of unique speakers (derived from the number of unique clusters) and assigning a speaker label to each embedding (or speech segment).
Process of identifying speaker segments from speech activity embeddings. | scraping/output/5364586834657158796.txt | [
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Process of identifying speaker segments from speech activity embeddings.
### End-to-end diarization systems
Here, the individual submodules of the traditional speaker diarization system
can be replaced by one neural network that is trained end-to-end on speaker
diarization.
#### Advantages
➕ Direct optimization of the network towards maximizing the accuracy for the
diarization task. This is in contrast with traditional systems where
submodules are optimized individually but not as a whole.
➕ Less need to come up with useful pre-processing and post-processing
transformation on the input data.
#### Disadvantages
➖ More effort needed for data collection and labelling. This is because this
type of approach requires speaker-aware transcripts for training. This differs
from traditional systems where only labels consisting of the speaker tag along
with the audio timestamp are needed (without transcription efforts). | scraping/output/5364586834657158796.txt | [
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➖ These systems have the tendency to overfit on the training data.
### Speaker diarization frameworks
As you can see, there are advantages and disadvantages to both traditional and
end-to-end diarization systems. Building a speaker diarization system also
involves aggregating quite a few building blocks and the implementation can
seem daunting at first glance.
Luckily, there exists a plethora of libraries and packages that have all those
steps implemented and are ready for you to use out of the box 🔥.
I will focus on the most popular open-source speaker diarization libraries.
All but the last framework (UIS-RNN) are based on the traditional diarization
approach. Make sure to check out this link for a more exhaustive list of
different diarization libraries.
#### 1\. Pyannote
👉 Arguably one of the most popular libraries out there for speaker
diarization. | scraping/output/5364586834657158796.txt | [
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#### 1\. Pyannote
👉 Arguably one of the most popular libraries out there for speaker
diarization.
👉 Note that the pre-trained models are based on the VoxCeleb datasets which
consists of recordings of celebrities extracted from YouTube. The audio
quality of those recordings are crisp and clear, so you might need to retrain
your model if you want to tackle other types of data like recorded phone
calls.
➕ Comes with a set of available pre-trained models for the VAD, embedder and
segmentation model.
➕ The inference pipeline can identify multiple speakers speaking at the same
time (multi-label diarization).
➖ It is not possible to define the number of speakers before running the
clustering algorithm. This could lead to an over-estimation or under-
estimation of the number of speakers if they are known beforehand.
#### 2\. NVIDIA NeMo | scraping/output/5364586834657158796.txt | [
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] |
#### 2\. NVIDIA NeMo
👉The Nvidia NeMo toolkit has separate collections for Automatic Speech
Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS)
models.
👉 The models that the pre-trained networks were trained on VoxCeleb datasets
as well as the Fisher and SwitchBoard dataset, which consists of telephone
conversations in English. This makes it more suitable as a starting point for
fine-tuning a model for call-center use cases compared to the pre-trained
models used in pyannote. More information about the pre-trained models can be
found here.
➕ Diarization results can be combined easily with ASR outputs to generate
speaker-aware transcripts.
➕ Possibility to define the number of speakers beforehand if they are known,
resulting in a more accurate diarization output.
➕ The fact that the NeMo toolkit also includes NLP related frameworks makes it
easy to integrate the diarization outcome with downstream NLP tasks.
#### 3\. Simple Diarizer | scraping/output/5364586834657158796.txt | [
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] |
➕ The fact that the NeMo toolkit also includes NLP related frameworks makes it
easy to integrate the diarization outcome with downstream NLP tasks.
#### 3\. Simple Diarizer
👉 A simplified diarization pipeline that can be used for quick testing.
👉 Uses the same pre-trained models as pyannote.
➕ Similarly to Nvidia NeMo, there’s the option to define the number of
speakers beforehand.
➖ Unlike pyannote, this library does not include the option to fine tune the
pre-trained models, making it less suitable for specialized use cases.
#### 4\. SpeechBrain
👉 All-in-one conversational AI toolkit based on PyTorch.
➕ The SpeechBrain Ecosystem makes it easy to develop integrated speech
solutions with systems such ASR, speaker identification, speech enhancement,
speech separation and language identification.
➕ Large amount of pre-trained models for various tasks. Check out their
HuggingFace page for more information. | scraping/output/5364586834657158796.txt | [
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] |
➕ Large amount of pre-trained models for various tasks. Check out their
HuggingFace page for more information.
➕ Contains comprehensible tutorials for various speech building blocks to
easily get started.
➖ Diarization pipeline is still not fully implemented yet but this might
change in the future.
#### 5\. Kaldi
👉 Speech recognition toolkit that is mainly targeted towards researchers. It
is built with C++ and used to train speech recognition models and decode audio
from audio files.
👉 Pre-trained model is based on the CALLHOME dataset which consists of
telephone conversation between native English speakers in North America.
👉 Benefits from large community support. However, mainly targeted towards
researchers and less suitable for production ready-solutions.
➖ Relatively steep learning curve for beginners who don’t have a lot of
experience with speech recognition systems.
➖ Not suitable for a quick implementation of ASR/diarization systems.
#### 6\. UIS-RNN | scraping/output/5364586834657158796.txt | [
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➖ Not suitable for a quick implementation of ASR/diarization systems.
#### 6\. UIS-RNN
👉 A fully supervised end-to-end diarization model developed by Google.
👉 Both training and prediction require the usage of a GPU.
➕ Relatively easy to train if you have a large set of pre-labeled data.
➖ No-pretrained model is available, so you need to train it from scratch on
your custom transcribed data.
That’s quite some different frameworks! To make it easier to pick the right
one for your use case, I’ve created a simple flowchart that can get you
started on picking a suitable library depending on your use case.
Flowchart for choosing a framework suitable for your diarization use case.
### Demo
Alright, you’re probably very curious at this point to test out a few
diarization techniques yourself. Below is an example of diarization of this
audio sample using the pyannote framework.
Audio diarization of provided sample using the pyannote framework
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Audio diarization of provided sample using the pyannote framework
Make sure to check out the interactive version of this blogpost on Hugging
Face space to test out diarization on your own audio samples with different
diarization frameworks.
### Conclusions
In this blogpost we covered different aspects of speaker diarization.
* First we explained what speaker diarization is and gave a few examples of its different areas of applications.
* We discussed the two main types of systems for implementing diarization system with a solid (high-level) understanding of both traditional systems and end-to-end systems.
* Then, we gave a comparison of different diarization frameworks and provided a guide for picking the best one for your use case.
* Finally, we provided you with an example to quickly try out a few of the diarization libraries.
I hope you had a good time reading this article and learned some new stuff
along the way.
## Related posts
View all | scraping/output/5364586834657158796.txt | [
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] |
I hope you had a good time reading this article and learned some new stuff
along the way.
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# 3D object detection in the real world
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Share this post | scraping/output/-8232651017417611530.txt | [
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Benchmarking VoteNet and 3DETR for detecting objects in point clouds
Photo by Arisa Chattasa on Unsplash
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Benchmarking VoteNet and 3DETR for detecting objects in point clouds
Photo by Arisa Chattasa on Unsplash
Most computer vision applications today work with ‘flat’ two-dimensional
images like the ones you find in this medium blogpost, to great success. The
world is not flat, however, and adding a third dimension promises to not only
increase performance but also make possible entirely new applications. With 3D
data becoming more and more widely available that time may very well be now.
Some smartphones now feature Lidar sensors (acronym for “light detection and
ranging”, sometimes called “laser scanning”) while other cameras use RGB-D
cameras (an RGB-D image is the combination of a standard RGB image with its
associated “depth map”) like Kinect or Intel RealSense. 3D data allows a rich
spatial representation of the sensor’s surroundings and has applications in
robotics, smart home devices, driverless cars, medical imaging and many other
industries.
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In this blogpost, we explore Meta (Facebook)’s 3DETR and its predecessor
Votenet which present a clever approach to recognizing objects in a 3D point
cloud of a scene (see [1], [2] and [3] for the research articles). These
methods go beyond existing methods in that they fully account for available
depth information without increasing compute cost in a prohibitive manner. The
models’ objective is to use point clouds (preprocessed from RGB-D images) and
estimate oriented 3D bounding boxes as well as semantic classes of objects.
Depth map from Kinect sensor
### 3D data | scraping/output/-8232651017417611530.txt | [
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Depth map from Kinect sensor
### 3D data
Several formats are available for 3D data: RGB-D images, polygon meshes,
voxels and point clouds. A point cloud is simply an unordered set of
coordinate triplets (x, y, z). This format has become very popular as it
preserves all the original 3D information and does not use any discretization
or 2D projection. Fundamentally, 2D based methods cannot provide accurate 3D
position information which is problematic for many critical applications like
robotics and autonomous driving.
Hence applying machine learning techniques directly to point cloud inputs is
very appealing: it avoids geometric information loss that occurs when 2D
projections or voxelizations are performed. Thanks to the rich feature
representations inherent in 3D data, deep learning on point clouds has
attracted a lot of interest over the past few years. | scraping/output/-8232651017417611530.txt | [
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] |
There are challenges however. The input’s high dimensionality and unstructured
nature as well as the small size of available datasets and their levels of
noise poses difficulties. Moreover, point clouds are by nature occluded and
sparse: some parts of the 3D objects are simply hidden to the sensor or the
signal can be missed or blocked. Furthermore, point clouds are by nature
irregular, making 3D convolution very different from the 2D case.
To carry out our tests we chose the SUN RGB-D dataset. It includes 10,335
RGB-D images of indoor scenes (bedrooms, furniture stores, offices,
classrooms, bathrooms, labs, conference rooms, …). These scenes have been
annotated with 64,595 oriented 3D bounding boxes around 37 types of objects
including chairs, desks, pillows, sofas, … (see [4], [5], [6] and [7] for
details on the various sources of the dataset and the methodologies used to
create it).
### Data preprocessing | scraping/output/-8232651017417611530.txt | [
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] |
### Data preprocessing
The conversion of RGB-D images to cloud points is done via a linear
transformation of 2D coordinates and depth values at given coordinates in the
image while taking into account intrinsic characteristics of the camera. Basic
trigonometric considerations lead to the mathematical formulation of this
linear transformation (see [8] for a more detailed explanation). The following
image illustrates the operation. The preprocessing can be done using Matlab
functions like in the Facebook team’s code (some code changes are necessary to
get it to work with the free version Octave, which significantly slows down
the preprocessing) or using the Open3D open-source library (see [9] for a link
to the library’s homepage).
From depth map to point cloud (image by yodayoda Inc. in [8])
### Pointnet++ and VoteNet | scraping/output/-8232651017417611530.txt | [
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From depth map to point cloud (image by yodayoda Inc. in [8])
### Pointnet++ and VoteNet
The first methodology VoteNet [1] uses Pointnet++ [3] as a backbone (both by
Charles R. Qi). Pointnet++ takes a point cloud as an input and outputs a
subset of the input cloud but each point has more features and is enriched
with context about local geometric patterns. This is similar to convolutional
neural networks except that the input cloud is subsampled in a data-dependent
way: the neighborhood around a particular point is defined by a metric
distance and the number of points in that neighborhood is variable. The
following image (extract from [3]) illustrates the Pointnet++ architecture. | scraping/output/-8232651017417611530.txt | [
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The Pointnet layers on this image create abstractions of every local region
(defined by a fixed radius). Each local region is transformed into a vector
composed of its centroid and enriched features creating an abstract
representation of the neighborhood. In our particular case, the raw input
point cloud is made of a variable number (20,000 or 40,000) of triplets (x, y,
z), the output of the Pointnet++ backbone is a set of 1,024 points of
dimension 3+256. Each Pointnet layer in the backbone is simply a multilayer
perceptron (1 or 2 hidden layers each).
The VoteNet methodology for 3D object detection uses the output of Pointnet++
and applies “Deep Hough Voting”, a method which is illustrated by the
following image (extract from [1]). | scraping/output/-8232651017417611530.txt | [
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The VoteNet methodology for 3D object detection uses the output of Pointnet++
and applies “Deep Hough Voting”, a method which is illustrated by the
following image (extract from [1]).
Each point (with its enriched features) output by the backbone is fed into a
shared multilayer perceptron to generate a vote (the “voting module”): this
voting neural network outputs a displacement triplet between a point (its
input) and the centroid of the object it belongs to (if any). It is trained to
minimize the norm of that displacement and adds some extra features supposed
to help the vote aggregation. | scraping/output/-8232651017417611530.txt | [
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As shown in the image above, the votes are then clustered. Each cluster is fed
to a “proposal and classification module” (two multilayer perceptrons in
practice) which outputs a prediction vector including: an objectness score,
bounding box parameters and semantic classification scores. Each of those
three elements contributes to a loss function (so 4 in total if we add the
vote regression loss mentioned above): an objectness cross-entropy loss, a
bounding box estimation loss and a class prediction loss.
### 3DETR
The 3DETR method (described in [2]) is a pure transformer-based approach with
hardly any modification compared to the vanilla transformer architecture which
is quite remarkable. The 3DETR architecture is described in the following
image (extract from [6]).
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The transformer encoder receives inputs from a subsampling + set aggregation
layer like in the Pointnet++ backbone described above except that the
operation is applied only once in this case instead of several times in
Pointnet++. The transformer encoder then applies several layers of self-
attention and non-linear projections (in our case 3 multihead attention layers
with 8 heads each). No positional embedding is necessary as this information
is already included in the inputs. The self-attention mechanism is
permutation-invariant and allows the representation of long range
dependencies. This being said, the self-attention layers in the encoder can be
modified with a mask in order to focus on local patterns rather than global
ones. | scraping/output/-8232651017417611530.txt | [
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The decoder, then, is composed of several transformer blocks (8 in our case).
It receives queries and predicts 3D bounding boxes. The queries are generated
by sampling some points (128 in our case) from the input cloud and feeding
them into a positional embedding layer followed by a multilayer perceptron.
Data augmentation is used during training by applying random sub-sampling,
flipping, rotation and random scaling of the point cloud.
#### Example
This is an example of RGB-D image from the SUN RGB-D dataset.
Input RGB image with 2D bounding boxes
Input depth map
The image then gets preprocessed into a point cloud of 20,000 or 80,000
points. You can use MeshLab to visualize all sorts of 3D data including point
clouds.
Processed into a point cloud
The VoteNet or 3DETR algorithm can now predict bounding boxes (and object
classes).
Predicted bounding boxes
### Performance | scraping/output/-8232651017417611530.txt | [
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Processed into a point cloud
The VoteNet or 3DETR algorithm can now predict bounding boxes (and object
classes).
Predicted bounding boxes
### Performance
The most widely used metric for assessing the performance of 3D object
detection techniques, is the mean average precision (mAP): average precision
(AP) is the area under the Precision-Recall curve and mean average precision
(mAP) is its average over all object classes. An IoU (Intersection over Union)
threshold is fixed at 0.25 or 0.5 giving us AP25 or AP50 metrics. This
controls the desired overlap between predicted bounding boxes and ground-truth
bounding boxes.
We have trained the VoteNet model for 180 epochs (as suggested by the authors
of [5]) on the SUN RGB-D training set and got an AP25 of 57% on the test set
(similar to [5]). Our VoteNet model is of reasonable size with around 1
million trainable parameters. | scraping/output/-8232651017417611530.txt | [
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The 3DETR model is larger with 7 million trainable parameters and it needs to
be trained for 360 epochs to reach an AP25 of 57% on the SUN RGB-D dataset.
This would have taken several days of training. Luckily, the authors of [6]
have made public a model that has been pre-trained for 1080 epochs on SUN
RGB-D. We have tested it and got the same AP25 as VoteNet i.e. 57%. A version
of the 3DETR model with masked self-attention in the encoder is available as
well and gets a slightly better performance. It should be noted that,
according to the authors of [6], the performance gain is more important on
another dataset (ScanNetV2 see more on this dataset below).
### Transfer learning
An important consideration is the ability to fine-tune pre-trained models like
those provided by the authors of [5] and [6] on our clients’ data. This is
particularly important in the case of 3D object detection where data is
difficult to annotate, occluded and noisy. | scraping/output/-8232651017417611530.txt | [
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We tested the transferability of a VoteNet trained on the ScanNetV2 dataset to
the SUN RGB-D dataset. ScanNetV2 (see [10] for details) is an annotated
dataset of 1,200 3D meshes reconstructed from indoor scenes. It does include
18 object categories While both SUN RGB-D and ScanNetV2 belong to a similar
domain of indoor scenes, they are in effect quite different: scenes in
ScanNetV2 cover larger surfaces, are more complete and hold more objects.
Vertices in the ScanNetV2 dataset are sampled to create input point clouds.
We used a VoteNet model pre-trained on ScanNetV2 for 180 epochs. We kept as
much as we could from this model: the backbone module, the voting module and
all the proposal and classification module except its last output layer.
Interestingly it took only 30 epochs of fine-tuning on SUN RGB-D for this
model to achieve the same performance as the same VoteNet model trained from
scratch on SUN RGB-D for 180 epochs. | scraping/output/-8232651017417611530.txt | [
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] |
This is an encouraging result that makes us confident that our pre-trained
models can easily be transferred to ML6 clients’ data from other types of
indoor domains with no need for large annotated datasets.
Links
[1] Deep Hough Voting for 3D Object Detection in Point Clouds
[2] https://openaccess.thecvf.com/content/ICCV2021/html/Misra_An_End-to-
End_Transformer_Model_for_3D_Object_Detection_ICCV_2021_paper.html
[3] PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric
Space
[4] NYU Depth Dataset V2 — Nathan Silberman
[5] Berkeley 3-D Object Dataset
[6] SUN3D dataset
[7] SUN RGB-D
[8] From depth map to point cloud. How to convert a RGBD image to points… | by
yodayoda | Map for Robots | Medium
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Structured Data | scraping/output/-8232651017417611530.txt | [
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] |
Large Language Model
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People
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# Front-End Development | scraping/output/2976252341011593358.txt | [
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Contact: | scraping/output/-7841582850245623109.txt | [
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# Ethical and Technical Assessment for an AI-based solution in the EU Council
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the Council of the EU
# Ethical and Technical Assessment for an AI-based solution in the EU Council
Newsroom team
## Impact
In support of the Newsroom team of the General Secretariat of the Council of
the EU, ML6 evaluated the technical feasibility and potential ethical risks
involved in using an AI-based solution for annotating media assets. ML6 used
their risk assessment methodology to assess the key ethical challenges based
on the 7 dimensions of Trustworthy AI and give recommendations to mitigate
potential risks already in the design of a technical solution. With these
recommendations in mind, the EU Council Newsroom team will be able to make
informed decisions about the design and implementation of an AI-based solution
for their Newsroom.
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At ML6, we believe that assessing AI solutions systematically for ethical
risks is an important part of AI design and development and should be part of
any AI project. With this project, the EU council Newsroom team role-models
the practice of doing such assessments already in an early stage.
## Intro to the customer | scraping/output/-1787529068431348079.txt | [
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## Intro to the customer
The European Council and the Council of the European Union are two of the main
institutions of the European Union (EU). The European Council is composed of
Heads of state or government of the 27 EU member states, the European Council
President and the President of the European Commission, and defines the
political direction and priorities of the EU. The Council of the EU is
composed of national government ministers from each member state. The Council
negotiates and adopts EU laws, in most cases together with the European
Parliament. The General Secretariat of the Council serves both of these
institutions. Via the Newsroom website, this institution provides free of
charge access to high-quality video, image and audio files covering all
significant activities of the EU Council, such as roundtable discussions and
press conferences.
## Challenge | scraping/output/-1787529068431348079.txt | [
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## Challenge
To increase efficiency, free up time of the EU Council Newsroom and allow them
to focus on more valuable tasks, the team is considering the use of an AI-
based system to automate the process of annotating media files. Seeing the
importance of and need for building ethical and trustworthy AI applications,
the EU council wanted to be a forerunner in accompanying the technical design
of an AI solution with an in-depth ethical assessment.
By
## Solution
During the conceptual phase of the project, significant emphasis was placed on
identifying possible ethical risks of an AI-based system and the EU Council
team worked closely with ML6 to gain technical and ethical recommendations.
These included considerations such as the safety and the technical robustness
of the system, data privacy and governance, fairness and diversity. These
findings served to guide possible solutions and design choices, ensuring
potential obstacles in the future can be averted.
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In particular, when it came to ethical recommendations, we evaluated potential
ethical risks based on our risk assessment methodology using the seven
dimensions of trustworthy AI as defined by the EU high-level expert group on
AI. Trustworthy AI describes AI that is lawful compliant, ethically
responsible and technically reliable. The concept is grounded on the premise
that AI will reach its full potential only when trust can be established
throughout its entire lifecycle, from conception and development to deployment
and usage. One approach to achieving trustworthy AI is by aligning the design
and application of AI with ethical and inclusive values and striving to
maximise its benefits while simultaneously identifying, preventing and
mitigating potential pitfalls.
By
## Results | scraping/output/-1787529068431348079.txt | [
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] |
By
## Results
The assessment process started with a series of interviews with members of the
Newsroom team, which provided valuable insights into their daily tasks and
processes. These interviews have been followed by an in-depth technical and
ethical assessment of potential solutions. In addition, the ML6 team also
carried out various technical experiments to gather a deeper understanding of
the topic at hand. All findings were summarised in a report, serving as
documentation of potential risks and risk mitigation strategies.
Accelerating businesses with AI technology & experts
Contact:
info@ml6.eu
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References | scraping/output/-1787529068431348079.txt | [
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Share this post | scraping/output/9169651862203302984.txt | [
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Deploying any Machine Learning model into production requires a thorough
consideration of three major factors:
* Performance metrics
* Latency
* Deployment cost
While numerous articles cover training, fine-tuning, and explaining the paper
behind Whisper, there is a scarcity of resources focusing on running Whisper
in a production environment. Embarking on a two-part blog series, we delve
into the practicalities of implementing OpenAI Whisper in a production
environment. In the first part, we navigate the tradeoffs between model sizes
and GPUs, shedding light on optimal choices. The sequel takes a closer look at
the transformative effects of tools and techniques like JAX, ONNX, and KernlAI
on these metrics. | scraping/output/9169651862203302984.txt | [
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Using HuggingFace's Whisper implementation, we benchmarked multilingual models
across different batch sizes (1,2,4,8,and 16) on CPUs and GPUs (T4,V100,and
A100) to evaluate inference speed. All the benchmarks were done on the test
split of the HuggingFace dataset: librispeech_asr.
Key Findings:
* As the size of the Whisper model increases, the inference time becomes slower since larger models have more parameters.
* Running Whisper on CPUs is noticeably slower compared to using GPUs.
* Irrespective of the model size, the inference time is the quickest on A100.
To sum it up: The T4 GPU emerges as the optimal choice for supporting any
Whisper model (except Whisper large-v2) in online (Batch-size = 1) and batch
settings. It offers a cost-effective solution compared to the P100 and A100
GPUs. Despite the P100’s superior speed in batch settings compared to the T4,
the tradeoff of higher costs makes it a less economical choice.
Read the full article on our Medium account.
| scraping/output/9169651862203302984.txt | [
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] |
Read the full article on our Medium account.
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Employee Log in Candidate Log in to Connect Homepage ml6.eu
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# Kaat Van Doren
Head Talent & Culture – Talent & Culture 🫶
kaat.van.doren@ml6.eu
As Head of Talent & Culture at ML6, Kaat focuses on accelerating all ML6
Agents by being a trustful Business Partner. Together with the whole HR team,
she integrates the five people pillars of ML6 (ML6 DNA, Meaningful work, Other
People, Appreciation & Personal Path) in all projects & HR related activities. | scraping/output/-240747374499063990.txt | [
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] |
Her focus is on personal development & soft HR in combination with HR
strategy. Next to this she guides the HR colleagues on Recruitment and
Compensation & Benefits. The combination of People and Technology is the asset
in her role at ML6. She treasures and admires the people with whom she can
collaborate.
Kaat is very passionate about people and this started already during her
studies in Human Resource Management at AMS. After this she started her career
as a recruitment consultant at Hays with a strong focus on IT profiles.
Therefore she dares to talk tech and dig deep into her collaboration with ML6
agents.
Have a look at her latest blogposts:
"Doubling down on learning and innovation"
## Work with me
* Talent Reward Officer Talent & Culture 🫶 * Multiple locations
* Junior Talent Partner Talent & Culture 🫶 * Multiple locations
## Some of my colleagues
Julie Plusquin
Talent Partner
## About ML6 | scraping/output/-240747374499063990.txt | [
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## Some of my colleagues
Julie Plusquin
Talent Partner
## About ML6
We guide the AI revolution towards positive impact. 🌐
Exciting developments are happening in the world of AI, offering unprecedented
opportunities for businesses to innovate and gain a competitive edge. Now more
than ever is the time to create intelligence with lasting positive impact. And
you can be a part of this!
1. We deliver end-to-end projects where we accompany organisations through the entire AI journey
2. We build bespoke hybrid ML solutions
3. We navigate and set up advisory projects
As ML6 highly values diversity, inclusion and transparent communication, we've
implemented a formal channel to report serious misconduct here.
Examples of serious misconduct (not limited to): safety of the individual
(aggression, discrimination, sexual harassment), data privacy issues, public
health, tax and fraud.
* Join us 🕵️♀️
* Data & privacy
* Manage cookies
ml6.eu/
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No items found.
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# Jens Bontinck
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# Jens Bontinck
No items found.
Jens Bontinck is the Head of Delivery & Advice at ML6, leading a team of 70 AI
and Data experts in creating top-quality AI solutions and providing guidance.
With a strong background in technology and AI strategy, he is a highly
knowledgeable and passionate tech leader. Jens regularly shares his insights
as a guest lecturer at esteemed institutions like Vlerick Business School and
UGent, and through his blog. He challenges the status quo and shares his
honest thoughts on the current and future evolutions of AI. | scraping/output/4463436619648346933.txt | [
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] |
Jens kickstarted his career as a developer at RealDolmen, after he graduated
from Ghent University as Master of Science in Information Engineering
Technology. Over time he built experience on a functional and strategic level
and focused on Digital Strategy at LoQutus. Having a clear understanding of
both the technical and strategic domain, Jens is able to deliver innovative
and competitive insights on a day-to-day basis.
You can follow Jens’ latests thoughts via jensbontinck.be
Visit my LinkedIn profile
Our know-how
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August 10, 2023 | scraping/output/4463436619648346933.txt | [
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] |
February 13, 2024
By
Jens Bontinck
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Is your enterprise architecture team slowing down innovation? | scraping/output/4463436619648346933.txt | [
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April 18, 2023
By
Jens Bontinck
Generative AI
Placeholder tag
Is your enterprise architecture team slowing down innovation?
With our Advisory services, we support multiple clients in speeding up the
adoption of AI and Advanced Analytics by giving concrete recommendations for
their data & cloud architecture and application landscape. In Koen's latest
blogpost, he explains our view on the modern enterprise team and shares five
tips and tricks on how to make the most of your data and organize for success.
March 2, 2021
By
Jens Bontinck
Strategic Advice
Placeholder tag
Data Mess? Or Data Mesh?
Data is the “fuel” for ML. Yet, in many projects, it is still challenging to
get the data right due to a lack of documentation, data quality issues,
missing historical data, scalability issues with data platforms and overall
lack of ownership.
February 16, 2021
By
Jens Bontinck
Large Language Model
Foundation Models
Corporate
People
Structured Data
Chat GPT
Sustainability | scraping/output/4463436619648346933.txt | [
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# 3D Computer Vision: Unlocking the Third Dimension [Part 1]
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Oops! Something went wrong while submitting the form. | scraping/output/4872459060818682608.txt | [
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This blog post is the first part of our on-going blog series about 3D computer
vision. This first article (Part 1) provides an overview of 3D computer vision
and how this technology is being used today, thus suitable for a diverse
audience interested in the subject matter. The subsequent blog posts (Part 2
and 3) are tailored for a more technical audience and for those seeking to
implement 3D computer vision applications.
### Introduction | scraping/output/4872459060818682608.txt | [
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### Introduction
In today’s fast-paced world of technology, it’s more important than ever to
understand and interpret the details of our surroundings. In recent years,
we’ve seen Convolutional Neural Networks (or CNNs, for short) completely
change computer vision, allowing us to analyze images with incredible
accuracy. As automation, robotics, and retail applications continue to grow,
so does the demand for more advanced vision systems. This is where 3D Computer
Vision shines, introducing depth information and a level of understanding that
was once out of reach for traditional 2D computer vision systems. | scraping/output/4872459060818682608.txt | [
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In our upcoming series of blog posts, we’ll dive deep into the advantages of
3D computer vision and explore how this technology is transforming various
sectors. By approaching this topic through the framework of a typical machine
learning pipeline (Figure 1), we will gain insights into the process of
capturing three-dimensional data, investigate the diverse sensors involved,
and ultimately explore the multitude of methods for processing and extracting
value from this information.
Figure 1: The machine learning pipeline. In this blog post series we dive into
how 3D computer vision is done at each step of the way.
In this first part of the series, we uncover the exciting world of 3D computer
vision, its real-life applications, and how it’s shaping the future of
numerous industries.
### 2D vs 3D Computer Vision | scraping/output/4872459060818682608.txt | [
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### 2D vs 3D Computer Vision
To truly appreciate the benefits of 3D computer vision, it’s essential to
understand the differences between 2D and 3D computer vision. At its core,
computer vision is a technology that processes and interprets visual data. In
2D computer vision, data is analyzed based on pixel values, colors, and
textures in a flat, two-dimensional image, much like how we view photographs.
While it has been highly successful in tasks like image recognition and
classification, it falls short when it comes to understanding spatial
relationships and depth, making it less suitable for tasks that require
accurate perception of real-world environments. By providing depth
information, 3D computer vision can address many of the limitations faced by
2D computer vision, such as understanding spatial relationships, handling
occlusion, and overcoming issues related to lighting and shadows. | scraping/output/4872459060818682608.txt | [
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To help you see the differences between 2D and 3D computer vision, let’s use a
simple, everyday example. Picture yourself looking at a photo of a cozy living
room, complete with furniture arranged in various spots. With 2D computer
vision, it’s easy to identify and recognize the different pieces of furniture
and their colors. However, figuring out the relative distances between the
objects and their actual sizes can be tricky since there’s no depth
information. As humans, we have to rely on visual cues (Figure 2) like
shadows, perspective and overlapping objects to make sense of depth in a 2D
image; but these cues aren’t always clear-cut.
Figure 2: Monocular depth cues. For an interesting example of how perspective
and visual cues can be deceiving, check out the Ames room. | scraping/output/4872459060818682608.txt | [
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Figure 2: Monocular depth cues. For an interesting example of how perspective
and visual cues can be deceiving, check out the Ames room.
Now, imagine actually stepping into that same living room. Your understanding
of the room, furniture, and their positions in relation to each other suddenly
becomes much clearer, thanks to the binocular depth cues our vision provides
(our ability to perceive depth using both eyes). This is the kind of enhanced
perception that 3D computer vision offers to machines, making it easier for
them to understand and interact with their surroundings. This ability is vital
in various tasks, including robotic navigation, object manipulation, and
accurate volume and shape measurements, enabling machines to interact with and
respond to the world more effectively. | scraping/output/4872459060818682608.txt | [
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The depth information provided by 3D computer vision also plays a critical
role in improving accuracy. While 2D computer vision can sometimes struggle to
differentiate between objects in a cluttered environment, 3D computer vision
leverages depth data to distinguish between them, ensuring tasks are carried
out with greater precision and reliability (Figure 3).
Figure 3: Using 3D vision to distinguish different products in a cluttered
environment (Source)
Another noteworthy advantage of 3D computer vision is its robustness to
lighting and shadows. In the world of 2D computer vision, changes in lighting
conditions and the presence of shadows can significantly impact performance,
as it relies solely on color and intensity data. However, utilizing depth
information allows us to easily overcome these issues. Overall, 3D computer
vision provides a strong resilience across a wide range of environments and
lighting conditions that allow systems to perform more consistently and
reliably. | scraping/output/4872459060818682608.txt | [
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0.039727650582790375
] |
So far, we have seen that 3D vision systems offer numerous advantages over 2D
systems by providing an additional layer of information, which can improve
performance. However, they also introduce complexities in terms of hardware
setup, storage capacity, and processing times. It’s crucial to assess the
specific application needs and determine if the benefits of using 3D vision
outweigh the challenges. To help guide this decision-making process, in the
following section, we explore how 3D data unlocks new possibilities and
applications across multiple industries.
# Real-World Applications and Trends | scraping/output/4872459060818682608.txt | [
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# Real-World Applications and Trends
3D computer vision is making a significant impact across various industries by
offering new possibilities and transforming traditional tasks. A big part of
this transformation was also possible due to the advancements in deep learning
models, where new model architectures and collection of more and more data
have been supporting significant improvements in the field. Let’s explore some
of the exciting applications and trends in several key sectors.
#### Manufacturing and Quality Control | scraping/output/4872459060818682608.txt | [
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In manufacturing, 3D computer vision is enhancing robotics and automation with
depth perception, allowing robots to better understand their surroundings and
perform tasks with increased precision, such as picking and placing items or
assembling components. Inline quality control and inspection also benefit
greatly from 3D computer vision and machine learning combined. 3D deep
learning models provide us with accurate object detection and recognition,
which can easily help systems identify defects, provide accurate and precise
measurements and identify inconsistencies in manufactured products with
greater reliability. This improved accuracy leads to higher product quality
and reduced waste, which is crucial for maintaining a competitive edge in
today’s fast-paced market. The integration of 3D computer vision with emerging
technologies like Industry 4.0 and the Internet of Things (IoT) is paving the
way for smart factories. Systems are becoming faster and more efficient and we | scraping/output/4872459060818682608.txt | [
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technologies like Industry 4.0 and the Internet of Things (IoT) is paving the
way for smart factories. Systems are becoming faster and more efficient and we
can expect to see more real-time processes integrated seamlessly into
manufacturing workflows. | scraping/output/4872459060818682608.txt | [
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Figure 4: Example of 3D quality inspection use case; measuring angle of lifted
can tabs (Source).
#### Autonomous Driving
In the automotive industry, 3D computer vision is essential for self-driving
cars, as it enables them to accurately perceive and understand their
environment. Companies like Waymo, Cruise and Zoox are using multimodal deep
learning models and advanced 3D vision technology for obstacle detection, lane
tracking, and navigation, paving the way for safer and more efficient
transportation. You can check this video for an interesting break down of how
Zoox uses computer vision to solve autonomous driving.
Figure 5: 3D mapping of surrounding environment for autonomous navigation
(Source).
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Figure 5: 3D mapping of surrounding environment for autonomous navigation
(Source).
#### Healthcare
Various medical applications, such as surgical assistance, diagnostics, and
medical imaging make use of 3D computer vision. For example, an anatomical
visualization service¹ creates 3D models of patients’ anatomy, assisting
surgeons in planning and executing procedures. During surgery, the model can
be viewed and manipulated on a console, improving surgical accuracy and
efficiency.
Figure 6: 3D anatomical models allowing doctors to plan and execute
procedures(Source)
#### Aerial Imagery | scraping/output/4872459060818682608.txt | [
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Figure 6: 3D anatomical models allowing doctors to plan and execute
procedures(Source)
#### Aerial Imagery
Drones equipped with 3D vision capabilities can provide detailed topographical
data, facilitating tasks like mapping, surveying, and environmental
monitoring². They also benefit agriculture by monitoring crop health,
analyzing soil conditions, and optimizing resource usage. This enables
precision farming practices, leading to increased yield and more sustainable
agriculture. Combining drones with 3D vision also allows for to safe
inspection of infrastructure and equipment like power grids, construction
sites and oil and gas refineries³. The 3D scanned models can be fed to a 3D
object detection
Figure 7: 3D inspection of a power grid (Source).
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Figure 7: 3D inspection of a power grid (Source).
#### Logistics
Retail and logistics are also experiencing the transformative power of 3D
computer vision. In inventory management, 3D computer vision can accurately
recognize and track individual items, even in cluttered environments, making
it easier to maintain accurate stock levels and optimize warehouse
organization. Furthermore, it can be integrated in optimization problems, such
as minimizing costs of packaging and shipping operations by scanning objects
dimensions and matching it with the available packaging space (e.g. in a
container).
#### Retail | scraping/output/4872459060818682608.txt | [
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#### Retail
In retail, the technology is being integrated into customer-facing
applications, such as virtual fitting rooms and augmented reality shopping
experiences, offering a more engaging and personalized experience for
consumers. Apple, for example, has LiDAR⁴ integrated in their iPhones’ Pro
versions, enabling a new range of applications. The IKEA Place app, for
example, allows users to visualize products in their homes before making a
purchase (check it out in this video).
Generative AI has also been making its way into the 3D space. Deep learning
models like pix2pix3D⁵ and Imagine 3D⁶ enable the creation of 3D
representations of objects using hand-drawn labels and textual prompts,
respectively. Although still in its early stages, this technology holds the
potential to unlock intriguing use-cases within the retail sector.
Figure 8: IKEA Place app allows users to try out different furniture in their
own space (Source).
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Figure 8: IKEA Place app allows users to try out different furniture in their
own space (Source).
As 3D computer vision continues to evolve, we can expect to see even more
innovative applications and trends emerging across various industries. The
ability to accurately perceive depth and spatial relationships not only
enhances existing processes but also unlocks new opportunities for businesses
to improve their operations and stay ahead of the competition.
### Conclusion | scraping/output/4872459060818682608.txt | [
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### Conclusion
As we have seen, 3D computer vision offers a wealth of advantages over
traditional 2D computer vision, opening new doors for innovation and improved
performance across a multitude of industries. While the manufacturing sector
stands to benefit significantly from the adoption of 3D computer vision
technologies, its impact extends far beyond this industry. The future of 3D
computer vision is marked by expanding possibilities and emerging applications
in diverse sectors such as retail, logistics, and even healthcare. By
embracing this transformative technology, companies can unlock new levels of
efficiency, productivity, and innovation, leveling up not only their
operations but also the industries they serve. | scraping/output/4872459060818682608.txt | [
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In conclusion, the adoption of 3D computer vision is not just a technological
leap, but a strategic move for forward-thinking businesses. It’s time to
explore the potential of 3D computer vision solutions for your organization
and stay ahead of the curve in an increasingly competitive landscape.
This first part of the blog post series served as an introduction to the world
of 3D vision. Keeping in mind the pipeline illustrated in Figure 1, our
upcoming post will explore the capture and storage of data in greater detail.
We will examine how this data is produced and consider how the selection of
sensor type may be influenced by diverse factors such as technical
requirements, environmental considerations, business constraints and other
relevant factors.
#### References
[1] — Iris, 3D anatomical visualization service: https://www.intuitive.com/en-
us/products-and-services/da-vinci/vision/iris | scraping/output/4872459060818682608.txt | [
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#### References
[1] — Iris, 3D anatomical visualization service: https://www.intuitive.com/en-
us/products-and-services/da-vinci/vision/iris
[2] — Parrot Drones and Autonomous Photogrammetry:
https://www.parrot.com/en/drones/anafi-ai
[3] — DJI Aerial Inspection of Infrastructure:
https://enterprise.dji.com/electricity/power-grid-management
[4] — LiDAR in Apple’s IPad Pro: https://www.apple.com/newsroom/2020/03/apple-
unveils-new-ipad-pro-with-lidar-scanner-and-trackpad-support-in-ipados/
[5] — Deng et al. “3D-aware Conditional Image Synthesis”. CVPR 2023.
[6] — Imagine 3D v1.2.
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Resource libraryEventTech Meetup Jobfair Berlin
# Tech Meetup Jobfair Berlin
April 18, 2024
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Meet up with ML6 at the Tech Meetup fair in Berlin on the 18th of April.
Join us in our mission to create impact with intelligent technology. | scraping/output/2285017380004148523.txt | [
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