GeoMesa is an open-source toolkit for processing and analyzing spatio-temporal data, such as IoT and sensor-produced observations, at scale. It provides a consistent API for querying and analyzing data on top of distributed databases (e.g. HBase, Accumulo, Bigtable, Cassandra) and messaging networks (e.g. Kafka) to handle batch analysis of historical archives of data and low-latency processing of data in-stream.
GeoMesa has deep integration with Spark SQL. It has added spatial types (e.g. Point, LineString, Polygons), spatial predicates (st_contains, st_intersects, etc.), and geometry processing functions (e.g. st_buffer, st_convexHull, etc.) to Spark SQL. It also optimizes the processing of these extensions by integrating with the Catalyst SQL optimizer to intercept SQL statements with spatial predicates and provision RDDs based on the underlying spatial index.
This session demonstrates the implementation of the GeoMesa Spark SQL integration, illustrate its application in production systems and demonstrate spatial aggregations and analytics using map-based visualizations.
GeoMesa on Apache Spark SQL with Anthony FoxDatabricks
GeoMesa is an open-source toolkit for processing and analyzing spatio-temporal data, such as IoT and sensor-produced observations, at scale. It provides a consistent API for querying and analyzing data on top of distributed databases (e.g. HBase, Accumulo, Bigtable, Cassandra) and messaging networks (e.g. Kafka) to handle batch analysis of historical archives of data and low-latency processing of data in-stream.
GeoMesa has deep integration with Spark SQL. It has added spatial types (e.g. Point, LineString, Polygons), spatial predicates (st_contains, st_intersects, etc.), and geometry processing functions (e.g. st_buffer, st_convexHull, etc.) to Spark SQL. It also optimizes the processing of these extensions by integrating with the Catalyst SQL optimizer to intercept SQL statements with spatial predicates and provision RDDs based on the underlying spatial index.
This session will describe the implementation of the GeoMesa Spark SQL integration, illustrate its application in production systems and demonstrate spatial aggregations and analytics using map-based visualizations.
Slides from my Introduction to PostGIS workshop at the FOSS4G conference in 2009. The material is available at http://revenant.ca/www/postgis/workshop/
GeoMesa is an open-source toolkit for processing and analyzing spatio-temporal data, such as IoT and sensor-produced observations, at scale. It provides a consistent API for querying and analyzing data on top of distributed databases (e.g. HBase, Accumulo, Bigtable, Cassandra) and messaging networks (e.g. Kafka) to handle batch analysis of historical archives of data and low-latency processing of data in-stream.
GeoMesa has deep integration with Spark SQL. It has added spatial types (e.g. Point, LineString, Polygons), spatial predicates (st_contains, st_intersects, etc.), and geometry processing functions (e.g. st_buffer, st_convexHull, etc.) to Spark SQL. It also optimizes the processing of these extensions by integrating with the Catalyst SQL optimizer to intercept SQL statements with spatial predicates and provision RDDs based on the underlying spatial index.
This session demonstrates the implementation of the GeoMesa Spark SQL integration, illustrate its application in production systems and demonstrate spatial aggregations and analytics using map-based visualizations.
GeoMesa on Apache Spark SQL with Anthony FoxDatabricks
GeoMesa is an open-source toolkit for processing and analyzing spatio-temporal data, such as IoT and sensor-produced observations, at scale. It provides a consistent API for querying and analyzing data on top of distributed databases (e.g. HBase, Accumulo, Bigtable, Cassandra) and messaging networks (e.g. Kafka) to handle batch analysis of historical archives of data and low-latency processing of data in-stream.
GeoMesa has deep integration with Spark SQL. It has added spatial types (e.g. Point, LineString, Polygons), spatial predicates (st_contains, st_intersects, etc.), and geometry processing functions (e.g. st_buffer, st_convexHull, etc.) to Spark SQL. It also optimizes the processing of these extensions by integrating with the Catalyst SQL optimizer to intercept SQL statements with spatial predicates and provision RDDs based on the underlying spatial index.
This session will describe the implementation of the GeoMesa Spark SQL integration, illustrate its application in production systems and demonstrate spatial aggregations and analytics using map-based visualizations.
Slides from my Introduction to PostGIS workshop at the FOSS4G conference in 2009. The material is available at http://revenant.ca/www/postgis/workshop/
LSGAN - SIMPle(Simple Idea Meaningful Performance Level up)Hansol Kang
LSGAN은 기존의 GAN loss가 아닌 MSE loss를 사용하여, 더욱 realistic한 데이터를 생성함.
LSGAN 논문 리뷰 및 PyTorch 기반의 구현.
[참고]
Mao, Xudong, et al. "Least squares generative adversarial networks." Proceedings of the IEEE International Conference on Computer Vision. 2017.
Semantic search within Earth Observation products databases based on automati...Gasperi Jerome
Since 1972 and the launch of Landsat 1– the first Earth Observation civilian satellite - millions of images have been acquired all over the Earth by a constantly growing fleet of more and more sophisticated satellites. Generally, searching within this huge amount of Earth Observation (EO) images is limited by the description of the acquisition conditions stored in the related metadata files, i.e. Where (footprint), When (time of acquisition) and How (viewing angles, instrument, etc.). Thus the larger community of end users misses the What filter - i.e. a way to filter search in term of image content. RESTo [1] uses the iTag [2] footprint-based tagging system to enhance image metadata and hopefully provides a way to express semantic queries on images content in term of land use. We investigated the performance of RESTo against a 12 millions simulated Sentinel-2 granules database representative of the forthcoming French national mirror site of Sentinel products (PEPS).
GeoMesa presentation from LocationTech Tour - DC - November, 14th 2013. Presented by Anthony Fox (@algoriffic) of CCRi.
GeoMesa is an open source project providing spatio-temporal indexing, querying, and visualizing capabilities to Accumulo. Learn more at http://geomesa.github.io/
Andreas Zeitler (Vuframe): Virtual & Augmented Business: How to Discover and ...AugmentedWorldExpo
A talk from the Work Track at AWE USA 2017 - the largest conference for AR+VR in Santa Clara, California May 31- June 2, 2017.
Andreas Zeitler (Vuframe): Virtual & Augmented Business: How to Discover and Leverage Immersive DataYou Already Own.
A good, practical and user-friendly Augmented or Virtual Reality application can provide clear return on investment for many businesses. Adopting software-driven solutions while the hardware in the market matures will give you a competitive edge while others are still catching up. However, there's still a major roadblock: where do you get data in the formats required for AR & VR? Realtime 3D, 360 Media, Point Cloud Data are neither cheap nor easy to come by – especially when it needs to be highly optimized to run on portable devices with limited battery life and performance. This workshop will illustrate how data which already exists in a lot of businesses can be leveraged in the age of Augmented and Virtual Reality. We will uncover which data sources already exist in your company and how you can deploy them as part of a cutting-edge virtual tool – within days, not weeks or months.
http://AugmentedWorldExpo.com
"Wix Engineering Media AI Photo Studio", Mykola MykhailychFwdays
In this talk, we will review components of the Wix Engineering AI-based image processing toolbox: super-resolution, automatic enhancement, cutout, etc. We'll share insights about models in production and their metrics. We'll show how ML models can improve user experience and make core products even more helpful.
AMF305_Autonomous Driving Algorithm Development on Amazon AIAmazon Web Services
Over the next decade, accelerating autonomous driving technology—including advances in artificial intelligence, sensors, cameras, radar and data analytics—are set to transform how we commute. In this session, you learn how to use Amazon AI for a highly productive, on demand, and scalable autonomous driving development environment. We compare the most popular AI frameworks including TensorFlow and MXNet for use in autonomous driving workloads. You learn about the AWS optimizations on MXNet that yield near linear scalability for training deep neural networks and convolutional neural networks. We demonstrate the ease of getting started on AWS AI by using a sample training dataset for building an object detection model on AWS. This session is intended for audiences who have some exposure to the underlying concepts for AI-based autonomous driving development. After attending the session, you can get started with AI development on AWS by using a sample dataset for building an object detection model.
Drones generate vast amounts of data, which is usually in the form of images or video streams. Identification of objects of interest, counting them, or detecting change over time, are some of the tasks that are monotonous and labor intensive.
FlytBase AI platform offers a complete solution to automate such tasks. It has been designed and optimised specifically for drone applications.
Discovering Your AI Super Powers - Tips and Tricks to Jumpstart your AI ProjectsWee Hyong Tok
In this session, we will share about cutting-edge deep learning innovations, and present emerging trends in the AI community. This session is for data scientists, developers who have a keen interest in getting started in an AI project, and wants to learn the tools of the trade. We will draw on practical experiences from working on various AI projects, and share the key learning, and pitfalls
LSGAN - SIMPle(Simple Idea Meaningful Performance Level up)Hansol Kang
LSGAN은 기존의 GAN loss가 아닌 MSE loss를 사용하여, 더욱 realistic한 데이터를 생성함.
LSGAN 논문 리뷰 및 PyTorch 기반의 구현.
[참고]
Mao, Xudong, et al. "Least squares generative adversarial networks." Proceedings of the IEEE International Conference on Computer Vision. 2017.
Semantic search within Earth Observation products databases based on automati...Gasperi Jerome
Since 1972 and the launch of Landsat 1– the first Earth Observation civilian satellite - millions of images have been acquired all over the Earth by a constantly growing fleet of more and more sophisticated satellites. Generally, searching within this huge amount of Earth Observation (EO) images is limited by the description of the acquisition conditions stored in the related metadata files, i.e. Where (footprint), When (time of acquisition) and How (viewing angles, instrument, etc.). Thus the larger community of end users misses the What filter - i.e. a way to filter search in term of image content. RESTo [1] uses the iTag [2] footprint-based tagging system to enhance image metadata and hopefully provides a way to express semantic queries on images content in term of land use. We investigated the performance of RESTo against a 12 millions simulated Sentinel-2 granules database representative of the forthcoming French national mirror site of Sentinel products (PEPS).
GeoMesa presentation from LocationTech Tour - DC - November, 14th 2013. Presented by Anthony Fox (@algoriffic) of CCRi.
GeoMesa is an open source project providing spatio-temporal indexing, querying, and visualizing capabilities to Accumulo. Learn more at http://geomesa.github.io/
Andreas Zeitler (Vuframe): Virtual & Augmented Business: How to Discover and ...AugmentedWorldExpo
A talk from the Work Track at AWE USA 2017 - the largest conference for AR+VR in Santa Clara, California May 31- June 2, 2017.
Andreas Zeitler (Vuframe): Virtual & Augmented Business: How to Discover and Leverage Immersive DataYou Already Own.
A good, practical and user-friendly Augmented or Virtual Reality application can provide clear return on investment for many businesses. Adopting software-driven solutions while the hardware in the market matures will give you a competitive edge while others are still catching up. However, there's still a major roadblock: where do you get data in the formats required for AR & VR? Realtime 3D, 360 Media, Point Cloud Data are neither cheap nor easy to come by – especially when it needs to be highly optimized to run on portable devices with limited battery life and performance. This workshop will illustrate how data which already exists in a lot of businesses can be leveraged in the age of Augmented and Virtual Reality. We will uncover which data sources already exist in your company and how you can deploy them as part of a cutting-edge virtual tool – within days, not weeks or months.
http://AugmentedWorldExpo.com
"Wix Engineering Media AI Photo Studio", Mykola MykhailychFwdays
In this talk, we will review components of the Wix Engineering AI-based image processing toolbox: super-resolution, automatic enhancement, cutout, etc. We'll share insights about models in production and their metrics. We'll show how ML models can improve user experience and make core products even more helpful.
AMF305_Autonomous Driving Algorithm Development on Amazon AIAmazon Web Services
Over the next decade, accelerating autonomous driving technology—including advances in artificial intelligence, sensors, cameras, radar and data analytics—are set to transform how we commute. In this session, you learn how to use Amazon AI for a highly productive, on demand, and scalable autonomous driving development environment. We compare the most popular AI frameworks including TensorFlow and MXNet for use in autonomous driving workloads. You learn about the AWS optimizations on MXNet that yield near linear scalability for training deep neural networks and convolutional neural networks. We demonstrate the ease of getting started on AWS AI by using a sample training dataset for building an object detection model on AWS. This session is intended for audiences who have some exposure to the underlying concepts for AI-based autonomous driving development. After attending the session, you can get started with AI development on AWS by using a sample dataset for building an object detection model.
Drones generate vast amounts of data, which is usually in the form of images or video streams. Identification of objects of interest, counting them, or detecting change over time, are some of the tasks that are monotonous and labor intensive.
FlytBase AI platform offers a complete solution to automate such tasks. It has been designed and optimised specifically for drone applications.
Discovering Your AI Super Powers - Tips and Tricks to Jumpstart your AI ProjectsWee Hyong Tok
In this session, we will share about cutting-edge deep learning innovations, and present emerging trends in the AI community. This session is for data scientists, developers who have a keen interest in getting started in an AI project, and wants to learn the tools of the trade. We will draw on practical experiences from working on various AI projects, and share the key learning, and pitfalls
[한국어] Safe Multi-Agent Reinforcement Learning for Autonomous DrivingKiho Suh
모두의연구소에서 “Safe, Multi-agent Reinforcement Learning for Autonomous Driving”이라는 논문을 발표한 자료를 공유합니다. 3월에 Intel에 $15.3 billion(우리나라 돈으로 약 17조원)에 인수된 Mobileye라는 이스라엘 회사의 CEO, VP of Engineering이 쓴 논문입니다. 그리고 얼마전에 Reddit에서 핫이슈가 된 논문인 “Failures of Deep Learning”을 발표한 분들이기도 하고요.
이 논문에서 풀어보자 하는것은 “사람처럼 운전하기” 입니다. 운전은 “multi-agent” game이고 도로에서 다른 agent들은 사람이기 때문에 자율주행자동차가 사람처럼 운전을 해야만 합니다. 운전은 multi-agnet이고 엄청난 양의 가능한 시니라오가 있어서 Markov Assumption이 성립이 안되어서 Imitation Learning, Policy Gradient without Markovity, 그리고 심지어 좀 고전적인(?) 방법인 dynamic programming까지 같이 써서 이 논문의 문제를 풀어봅니다.
My talk on Stegosploit at 44CON 2015:
Stegosploit creates a new way to encode "drive-by" browser exploits and deliver them through image files. These payloads are undetectable using current means. This paper discusses two broad underlying techniques used for image based exploit delivery - Steganography and Polyglots.
For details on Stegosploit, please visit http://stegosploit.info/
RenderMan*: The Role of Open Shading Language (OSL) with Intel® Advanced Vect...Intel® Software
This talk focuses on the newest release in RenderMan* 22.5 and its adoption at Pixar Animation Studios* for rendering future movies. With native support for Intel® Advanced Vector Extensions, Intel® Advanced Vector Extensions 2, and Intel® Advanced Vector Extensions 512, it includes enhanced library features, debugging support, and an extensive test framework.
Presentations from our AI Meetup #3 you can find below. We talked about soil segmentation, deep learning in Automotive, The Usage of the YOLO Algorithm for Traffic Participants Detection.
Presentations are really educational #machinelearning! #artificialintelligence #deeplearning #automotive #ai #novisadai
Similar to Training Drone Image Models with Grand Theft Auto (20)
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
8. ★ Machine vision models often require large amounts of labeled data to
train well
★ Existing labelled datasets can be too generic and have a broad concept
space for our purposes
9. ★ Machine vision models often require large amounts of labeled data to
train well
★ Existing labelled datasets can be too generic and have a broad concept
space for our purposes
10. ImageNet
14 million+ images of 21K+ class entities
YouTube-8M
450K+ hours of 4700+ class entities
Olga Russakovsky*, Jia Deng*, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma,
Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg
and Li Fei-Fei. (* = equal contribution) ImageNet Large Scale Visual Recognition
Challenge. IJCV, 2015.
Abu-El-Haija, Sami, et al. "YouTube-8M: A large-scale video classification
benchmark." arXiv preprint arXiv:1609.08675 (2016).
11. ImageNet
14 million+ images of 21K+ class entities
YouTube-8M
450K+ hours of 4700+ class entities
Olga Russakovsky*, Jia Deng*, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma,
Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg
and Li Fei-Fei. (* = equal contribution) ImageNet Large Scale Visual Recognition
Challenge. IJCV, 2015.
Abu-El-Haija, Sami, et al. "YouTube-8M: A large-scale video classification
benchmark." arXiv preprint arXiv:1609.08675 (2016).
12. ★ Graphics have become
extremely realistic over the
years
★ Games are codeable, enabling
complex simulations
★ Simulating in-game helps you
ignore low level tasks like
movement animations and
routing
13. ★ Graphics have become
extremely realistic over the
years
★ Games are codeable, enabling
complex simulations
★ Simulating in-game helps you
ignore low level tasks like
movement animations and
routing
14. ★ Graphics have become
extremely realistic over the
years
★ Games are codeable, enabling
complex simulations
★ Simulating in-game helps you
ignore low level tasks like
movement animations and
routing
15. ★ Rockstar Advanced Game
Engine’s (RAGE) super realistic
graphics
★ Huge modding community
provides lots of customization
★ Programmatically configurable
options
16. ★ Rockstar Advanced Game
Engine’s (RAGE) super realistic
graphics
★ Huge modding community
provides lots of customization
★ Programmatically configurable
options
17. ★ Rockstar Advanced Game
Engine’s (RAGE) super realistic
graphics
★ Huge modding community
provides lots of customization
★ Programmatically configurable
options
19. ★ Programmatically configurable
options
○ We can generate entities of choice
in-game and have them perform
complex actions
○ Vehicles: driving, turning, waiting at
stoplights
○ People: entering/exiting vehicles,
waiting to cross the street, parking
○ Environment: weather, time of day,
camera elevation, zoom
20. ★ Grand Theft Auto Dataset:
○ Video footage
○ Objects of interest per frame
(vehicles and pedestrians)
○ Object location information
(bounding box information)
○ Text Descriptions
(e.g. a white truck is turning left)
21. CNNS
★ Extracts features from the input image,
distilled down to class predictions
★ Preserves spatial relationship between
pixels
Bird
Airplane
Superman
Car
35. CNNS
★ Extracts features from the input image,
distilled down to class predictions
★ Preserves spatial relationship between
pixels
Bird
Airplane
Superman
Car
36. ★ YOLO9000 (YOLO v2) is a real time object
detection convolutional neural network
architecture
★ Redmon, Joseph and Farhadi, Ali. "YOLO9000:
better, faster, stronger." arXiv (2017).
37. ★ YOLO9000 (YOLO v2) is a real time object
detection convolutional neural network
architecture
★ Redmon, Joseph and Farhadi, Ali. "YOLO9000:
better, faster, stronger." arXiv (2017).
42. RNNs
★ Works well with sequential input (e.g. words in
a sentence or a vector of numbers representing
an image)
★ For a given input, incorporates a “feedback”
loop of the information it received and the
decision it made from the previous input in the
sequence
Neural
Network
Output
Input
43. “e”
“h”
Vocabulary of 4 letters:
h e l o
Letters could be encoded as:
h [1 0 0 0]
e [0 1 0 0]
l [0 0 1 0]
o [0 0 0 1]
h
e
e l
l l
l
o
49. Attention
★ Train model to focus on salient objects in
the image
★ Instead of feeding features from the
entire image to an RNN, just feed the
salient region’s features
54. Search: “red truck”
Search by Text in Video
★ Extracting captions from video and store
them in an index
★ Fast video search by text query over large
amounts of video
55. Search by Example in Video
★ A user-defined bounding box on a video
frame
★ Query for similar objects of interest in the
entirety of a video dataset, at the frame
level
56. Search by Example in Video
★ A user-defined bounding box on a video
frame
★ Query for similar objects of interest in the
entirety of a video dataset, at the frame
level
57. ★ GTA V allows us to create fully annotated, custom tailored,
photorealistic datasets
★ We can use this dataset to train models that are good at object
detection/localization, captioning, and search by example or text for
overhead video
★ The use of models trained on GTA data also has applicability in areas
such as real-time security camera alerting and self driving cars
58. ★ GTA V allows us to create fully annotated, custom tailored,
photorealistic datasets
★ We can use this dataset to train models that are good at object
detection/localization, captioning, and search by example or text for
overhead video
★ The use of models trained on GTA data also has applicability in areas
such as real-time security camera alerting and self driving cars
59. ★ GTA V allows us to create fully annotated, custom tailored,
photorealistic datasets
★ We can use this dataset to train models that are good at object
detection/localization, captioning, and search by example or text for
overhead video
★ The use of models trained on GTA data also has applicability in areas
such as real-time security camera alerting and self driving cars