I create content about deep learning | The DevRel and Deep Learning Dude

I don’t wanna hop on call to learn about or see a demo of what you’re building. Too busy hacking on VLMs, curating multimodal datasets, and contributing to the open source community. 

Harpreet Sahota 🥑

It was great to be back in Boston last week and host another awesome meetup!

Thank you for everyone who showed up, our awesome lineup of speakers for taking time out of their schedules for delivering such informative talks: Anku Rani Hanhui Wang Sage Simhon Sharut G. 

And for Voxel51 for sponsoring!

If you missed the event, checkout the comments for where you can find the recordings of the talks

🤖 Hacker-in-Residence @ Voxel51| 👨🏽‍💻 AI/ML Engineer | 👷🏽‍♀️ Technical Developer Advocate | Learn. Do. Write. Teach. Repeat.

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i built a ComfyUI-inspired visual, node-based workflow editor for FiftyOne

now you can build dataset curation, analysis, and model evaluation pipelines by connecting nodes on a canvas, without writing code

give it a try and let me know what you think, completely open source and all you have to is `pip install fiftyone` and follow the instructions on the readme

everyone is hype about gtc, but the 3d vision conference is this week and i'm much hype for that

i made a repo and a dataset to explore the papers

checkout the repo here:
 https://lnkd.in/gRFz6Kb7

here's a dataset that you can use to explore the papers:
 https://lnkd.in/g2RA87fD

it's been a long, long while since i've done a live stream on linkedin, but it's happening in t-mius 8 minutes!

if you're interested in learning about how to work with video datasets using open source tools, open data, and open models...this is the session for you!

Join us on Feb 26 for Exploring Video Datasets with FiftyOne and Vision-Language Models. Register for the Zoom: https://lnkd.in/gCk7gGPi

Video is the hardest modality to work with — more data, more complexity, and annotation workflows that don’t scale. This hands-on session by Harpreet Sahota 🥑 shows how to understand what’s inside massive video datasets without manually watching thousands of clips.

You’ll learn how to:
🎬 Navigate and explore video datasets in the FiftyOne App and understand dataset structure
🔎 Compute embeddings with Qwen3-VL to enable semantic search, zero-shot classification, and clustering
📝 Generate video descriptions and localize events using vision-language models like Qwen3-VL and Molmo2
🧭 Visualize patterns and trends through embedding projections in FiftyOne
📏 Evaluate model outputs against Action100M’s hierarchical annotations

Perfect for ML engineers, data scientists, and computer vision teams working with large-scale video data who want practical workflows for exploration, curation, and debugging.

#computervision #machinelearning #ai #datascience #videoanalytics #visionlanguagemodels #mlops #opensource

Derek Van de Streek, Swati Bannore Jain and 11 other attendees

Feb 26 - Exploring Video Datasets with FiftyOne and Vision-Language Models

excited to be back in stuttart! if you're in the area and want to speak at the meetup on april 21st, send me a dm

Chief Community Officer @ Voxel51 | Startup Advisor

Are you based in the Stuttgart area? Consider speaking at the upcoming April 21 - Stuttgart AI, ML and Computer Vision Meetup: https://voxel51.com/speak

#computervision #ai #artificialintelligence #machinevision
#machinelearning #datascience #opensource #stuttgart

i've learned a lot from Eric Ma over the years, from the first time I saw one of his talks on principled data science workflows to talks he's given for my communities over the years, and his latest work on getting the most from coding agents.

check out his next talk, and support the SciPy community as well!

Together with my teammates, we solve biological problems with network science, deep learning and Bayesian methods.

As a data scientist, how much of your week is spent on work a coding agent could do?

I once planned multiple days of work for a machine learning experiment last year. This was an experiment that I had put off for an entire quarter because, as a team lead, I didn't have the time to block out the 3-4 afternoons that I thought I'd need. 

Instead, I described the problem to Claude Opus 4.5 via OpenCode, and it wrote the code, ran the training, found issues, switched strategies, and finished the whole thing in 20 minutes, complete with a written report drafted *with* figures referenced.

The data scientists who figure this out now will have an edge.

I'm teaching a workshop on how to do this flavour of agentic data science to raise funds for SciPy 2026's Financial Aid Program. You'll learn:

- Prescriptive prompting for experiments
- Project structure for agents
- Logging and verification strategies to enable automated control
- Report writing with AI
- Staying in control during EDA
- The minimalist version first

These are the same techniques I use in my personal projects and my teammates to compress data science timelines from days to minutes. I think you'll have a chance to learn something good while doing something good!

Details:

- Date: March 27 & April 3, 2026
- Time: 2:00-4:00 PM EST
- Venue: Google Meet (calendar invite will be provided)
- 40 spots available
- $1,000 minimum donation, paid directly to NumFOCUS (tax-deductible), student pricing: $200!

Video recordings available if you can't attend live.

Sign up here: https://lnkd.in/eFjc36Tb. I'm looking forward to seeing what you'll build with these techniques! 

P.S. If you can't make it but share it with others who have the means and would benefit from the workshop, I would deeply appreciate it!

Open article: Agentic Data Science Workshop | SciPy 2026 Fundraiser by agent-assisted-data-science.vercel.app

Agentic Data Science Workshop | SciPy 2026 Fundraiser

We just released a new AWS workshop that I’m really excited to finally share 👇
https://lnkd.in/eiNprTd7

This came from something I keep seeing across autonomy, robotics, and computer vision teams lately.

Most teams are not blocked by models anymore.
They are blocked by data and iteration speed.

So we wanted to show what a practical workflow actually looks like:
🚗 generate synthetic driving data
 🧠 auto label using foundation models
 🔧 fine tune a small vision language model
 ☁️ deploy and iterate using AWS SageMaker
 👀 inspect and improve everything inside FiftyOne

The goal was simple.

Build something real that teams could follow end to end.

Huge credit to Ryan Patrick Sweeney, Mark Vinciguerra, and Derick Thompson for driving this forward, along with great collaboration between Amazon Web Services (AWS), NVIDIA Omniverse, and Voxel51 🙌

Also excited that Voxel51 is now live on AWS Marketplace, which makes getting started a lot easier for teams working on computer vision and robotics workflows!

Personally, I think this is how a lot of Physical AI systems will get built going forward.

Faster loops between data, models, and deployment instead of long waiting cycles.

If you’re working on robotics, AV, or multimodal systems, I’d genuinely love to hear what you’re building.

More coming soon 🚀

#PhysicalAI #ComputerVision #Robotics #MultimodalAI #SyntheticData #AWS #MachineLearning #AutonomousSystems

Open article: GitHub - aws-samples/sample-finetuning-small-vlms-synthetic-data by github.com

Contribute to aws-samples/sample-finetuning-small-vlms-synthetic-data development by creating an account on GitHub.

GitHub - aws-samples/sample-finetuning-small-vlms-synthetic-data

I’m so happy to have my boi Wiz present at one of our meets!

AI @ NVIDIA - 🏗️ Build | 🚢 Ship | 🚀 Share

View Chris "The Wiz 🪄" Alexiuk’s profile

skills.md - you've seen it, you've probably used it (I assume if you're following this LinkedIn account, you're someone who uses tools like OpenCode/etc.

I'm excited to be yappin' about skills at an upcoming Voxel51 / Hugging Face virtual event with a line up that boggles the mind: 

Ben Burtenshaw, Daniel Ávila Arias, and AdonAI Vera.

My presentation will focus on something near and dear to my heart: Skills as documentation. 

The days of documentation aren't over - humans, and agents, still need to understand how things work - but they're changing quickly. Not everyone wants to read a thorough getting started guide that may be built for a specific system (and if it's not, it'll be ~2K words long) Sometimes, you just want to clone a repository - and get cooking. 

Skills help bridge that gap - context for LLMs, but tailored for a human experience. 

Can't wait for the event, come on out and ask any skill questions you have!

looking forward to hosting this workshop tomorrow, join me!

Join Harpreet Sahota 🥑 on Feb 26 for a virtual workshop to learn how to use Facebook's Action100M dataset and FiftyOne to build an end-to-end workflow - https://lnkd.in/gFvwbQni

What you'll learn:

* Navigate and explore video data in the FiftyOne App, filter samples, and understand dataset structure

* Compute embeddings with Qwen3-VL to enable semantic search, zero-shot classification, and clustering

* Generate descriptions and localize events using vision-language models like Qwen3-VL and Molmo2

* Visualize patterns in your data through embedding projections and the FiftyOne App

* Evaluate model outputs against Action100M's hierarchical annotations to validate what the models actually capture

By the end of the session, you'll have a reusable toolkit for understanding any video dataset at scale, whether you're curating training data, debugging model performance, or exploring a new domain.

***********

Want to build better computer vision models? FiftyOne is an open source toolkit from Voxel51 (our Meetup sponsor) that helps you curate datasets, evaluate model performance, visualize embeddings, catch annotation errors, and eliminate duplicate images—all in one place.

“pip install fiftyone” is all it takes to get started - https://docs.voxel51.com/

#computervision #ai #artificialintelligence #machinevision #machinelearning #datascience #opensource

As an Artificial Intelligence Engineer at SHEALTH AI, I contribute to the development of advanced AI solutions, leveraging my expertise in computer vision and medical imaging. My recent work focuses on building and deploying sophisticated models to address real-world challenges.  

Currently pursuing an M.Sc. in Data Science at KES Shroff College, I am dedicated to advancing my knowledge and skills in AI and machine learning. My professional journey includes impactful experiences in machine learning, computer vision, and human behavior analysis, reflecting my commitment to innovation and data-driven problem-solving.

Joel Nadar

🚁 Transform Your Drone Videos into AI Training Data | Data Annotation for Aerial AI | Computer Vision

Drone technology is rapidly transforming industries like agriculture, security, construction, disaster management, and environmental monitoring.

But turning drone footage into actionable AI insights requires one essential step:

👉 High-quality data annotation.

Raw drone videos contain massive amounts of visual information, and converting them into structured training datasets is what enables powerful Computer Vision models.

🎯 Why Drone Video Annotation Matters

AI models trained on annotated aerial footage can power:

✔ Object detection from aerial views
✔ Traffic & crowd monitoring
✔ Agricultural crop health analysis
✔ Infrastructure inspection
✔ Wildlife tracking & environmental monitoring
✔ Disaster response and damage assessment

With drone footage covering large areas, accurate labeling is critical for reliable AI predictions.

🔍 Annotation Techniques for Drone-Based AI

Drone datasets often require advanced annotation methods to capture objects from top-down perspectives:

🎯 Bounding Box Annotation
🎯 Polygon Annotation
🎯 Instance Segmentation
🎯 Object Tracking Across Frames

These techniques allow ML models to accurately detect vehicles, buildings, people, crops, and other objects from aerial viewpoints.

🚀 Spotlight on Wisepl

Companies like Wisepl are helping transform raw drone videos into ML-ready datasets through scalable annotation services designed for Computer Vision applications.

Their solutions support teams building:

Smart city monitoring systems

Agricultural intelligence platforms

Infrastructure inspection AI

Environmental and wildlife monitoring tools

Security & surveillance analytics

👉 Check it out and explore more: https://www.wisepl.com/

If you're building Drone AI, Aerial Computer Vision, or Smart Monitoring Systems, high-quality annotated data is the foundation for accurate models.

👉 Follow Wisepl, Shajahan PC and Joel Nadar for more insights on Data Annotation, Computer Vision, and Machine Learning innovation 👍🏾

#DroneAI #AerialVision #DataAnnotation #ComputerVision #MachineLearning #SmartCities #AIInnovation #Wisepl

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Artificial Intelligence Consultant

Machine Learning & Computer Vision Independent Contractor

Teaching Assistant

Computer Vision Intern

AI Engineer | Consultant | Content Creator | Building What Matters | Open to Paid Collaborate 🤝🏾

English

Hindi

Wisepl

Data Annotation Manager at Wisepl Pvt Ltd | AI & ML | Computer Vision | Data Labeling | IT Support | ICT Specialist

🔍 Data Annotation for Quality Checking | AI-Powered Inspection | Computer Vision

In today’s AI-driven manufacturing and inspection systems, quality checking is becoming smarter and faster with Computer Vision.

But for AI models to accurately detect defects, irregularities, or product inconsistencies, they rely on one critical foundation:

👉 High-quality data annotation.

Well-annotated datasets allow Machine Learning models to understand what is acceptable quality vs. defective products.

🎯 Why Data Annotation is Critical for Quality Inspection

AI-powered quality checking systems can enable:

✔ Automated defect detection
✔ Manufacturing quality control
✔ Surface crack & damage detection
✔ Product sorting and classification
✔ Assembly line monitoring
✔ Packaging and labeling verification

With accurate annotations, AI systems can detect even the smallest defects in real-time production environments.

🔍 Annotation Techniques Used in Quality Checking AI

Quality inspection datasets often require multiple annotation techniques to capture different types of defects:

🎯 Bounding Box Annotation
🎯 Polygon Annotation
🎯 Instance Segmentation
🎯 Semantic Segmentation

These annotation methods help ML models detect scratches, dents, cracks, color inconsistencies, and structural defects with high precision.

🚀 Spotlight on Wisepl

Organizations like Wisepl are supporting AI teams with scalable data labeling services designed for quality inspection and industrial AI applications.

Their annotation services help companies develop:

Automated manufacturing inspection systems

Product defect detection AI

Smart factory monitoring solutions

Visual quality control platforms

👉 Check it out and explore more: https://www.wisepl.com/

As industries move toward AI-driven manufacturing and smart factories, high-quality annotated data becomes the backbone of reliable inspection systems.

👉 Follow Wisepl, Shajahan PC and Joel Nadar for more insights on Data Annotation, Computer Vision and Machine Learning innovation 👍🏾

#QualityInspection #DataAnnotation #MachineLearning #ComputerVision #IndustrialAI #SmartManufacturing #AIInnovation #Wisepl

🏭Attended today’s Roboflow webinar: AI for Auto Makers

In this live session, Roboflow's Michael Wrede showcased some of the most impactful real-world applications of Vision AI in automotive manufacturing.

The session highlighted how computer vision models can help manufacturers improve quality, efficiency, and reliability across the production pipeline.

🔎 Key use cases discussed:

• Part Presence Detection – Identify whether a component is present in the camera view during assembly
• Defect Detection – Detect issues like scuffs, wrinkles, and surface defects automatically
• Location Tracking – Return precise position data of parts or defects
• Assembly Verification – Ensure multiple components are correctly placed and assembled

We also explored instant segmentation techniques to measure gaps, seams, and tolerances helping factories automatically validate whether a component passes or fails quality inspection.

Another fascinating example showed how vision AI can monitor assembly tracks and detect unknown objects, preventing costly disruptions and reworks in production lines.

Exciting to see how computer vision is transforming automotive manufacturing from stamping and welding to final inspection.

🚀Looking forward to building multiple real-world computer vision use cases like these, especially in areas such as manufacturing quality inspection, defect detection, and automated process monitoring.

Patrick Deschere 
#ComputerVision #VisionAI #Roboflow #ManufacturingAI #IndustrialAI #MLOps #QualityInspection #EdgeAI #ArtificialIntelligence

Roboflow

Computer Vision @ Roboflow | Ex-USPS, Ex-Deloitte

Product Marketing @ Roboflow

☀️ Sunday Funday with Computer Vision 🚀

🔍 Exploring my Computer Vision workflow with FiftyOne + YOLO

Today I spent some time experimenting and visualizing a YOLO Ultralytics dataset using FiftyOne (Voxel51), one of my favorite tools for inspecting and debugging computer vision datasets.

Instead of training models blindly, it’s always important to analyze bounding boxes, labels, and dataset quality before starting the training process. A good dataset = a better model.

FiftyOne makes it much easier to:

• Visualize object detection datasets 📊
• Inspect bounding boxes 🔲
• Identify incorrect annotations ⚠️
• Debug datasets before training models 🧠

This workflow is extremely useful for Computer Vision Engineers, AI Researchers and ML teams working with YOLO-based pipelines.

Tools used:
🐍 Python
🧠 YOLO Dataset
🔍 FiftyOne Dataset Visualization

Big shoutout to Jimmy Guerrero and Harpreet Sahota 🥑 for the regular updates and amazing work around Voxel51 / FiftyOne I absolutely love this product. It’s incredibly helpful for anyone building real-world computer vision systems.

🎥 Watch the full recording here:
https://lnkd.in/dcVH36zh

If you're working in Computer Vision or AI, tools like FiftyOne can significantly improve your dataset quality and model performance.

Follow me Joel Nadar for more insights on AI, Computer Vision and real-world CV workflows.

Muhammad Rizwan Munawar Timothy Goebel Simran Baiche Arnaud Bastide Daniel Etukudo Felix Sam Nanor Jacqueline Bhaskar 

#ComputerVision #ArtificialIntelligence #YOLO #MachineLearning #AIEngineering #DataScience #AIWorkflow #DeepLearning #ComputerVisionEngineer #Voxel51 #FiftyOne

🌸 Happy International Women’s Day 🌸
Today we celebrate the strength, resilience, creativity and leadership of women across the world. Women continue to break barriers, inspire innovation, and shape the future in every field from science and technology to education, business and beyond. 🚀

Let us take a moment to appreciate the incredible contributions of women who empower communities, support families and drive positive change in society.

✨ Empowered women empower the world.
To all the amazing women out there thank you for inspiring us every day. Happy International Women’s Day.💜
#InternationalWomensDay #WomenInTech #WomenEmpowerment #WomenLeaders #WomenInSTEM #Equality #Inspiration #LinkedInFamily #Respect #CelebrateWomen

🚀RF-DETR Object Tracking Playground | AI Demo

I’ve been experimenting with object detection + tracking pipelines, and this time I explored something really exciting a Trackers Playground powered by RF-DETR (Roboflow).

In this demo, you can upload a video, detect COCO objects, and track them across frames using modern tracking algorithms. It’s a great way to understand how detection models and trackers work together in real-world computer vision systems.

🎥 Watch the full demo here:
https://lnkd.in/dcKTmVwt

🤗 Try the Trackers Playground:
https://lnkd.in/d-V6AJZr

🔍 What this demo shows
• Upload a video and run inference
• Detect objects using RF-DETR
• Track objects frame-by-frame
• Visualize real-time tracking results

This type of technology is widely used in:
⚽ Sports analytics
🚗 Traffic monitoring
🛍 Retail intelligence
🛡 Smart surveillance
🤖 Autonomous systems

I love exploring tools that make AI and computer vision more interactive and accessible and platforms like this help developers quickly test new ideas.

💡 I’m also open for collaborations research, developer support, project partnerships giveaways, and more.

If you’re interested in AI, Computer Vision and building real-world AI systems, let’s connect.

👍 Follow me Joel Nadar for more AI experiments and demos like this.
And if you enjoy this work, don’t forget to like and share.

Roboflow Joseph Nelson Grant Nelson Trevor Lynn Piotr Skalski 

#AI #ComputerVision #ObjectDetection #ObjectTracking #DeepLearning #MachineLearning #ArtificialIntelligence #RFDETR #OpenCV #PythonAI #AIEngineering #AIDevelopment #VisionAI #AIProjects #TechInnovation

Co-founder, CEO @ Roboflow

Product Manager. Thinking in systems, talking to users.

CMO & GTM Leadership

Open Source Lead @ Roboflow | Computer Vision | Vision Language Models

🌙 Late Night Curiosity → MLOps Learning

Last night I was just exploring my DagsHub account and thinking…

"What more can I actually build with this?" 🤔
Instead of just storing code, I started experimenting with something closer to real MLOps workflows.
So I decided to organize and manage my YOLO object detection models using DVC + DagsHub.
The result → a structured YOLO Models Repository with version-controlled models and reproducible experiments.

📂 Repository
https://lnkd.in/dh9EisAr

According to the documentation I prepared for this project, the repository contains multiple Ultralytics YOLOv8 models managed using Git + DVC + DagsHub storage, allowing efficient model versioning without bloating the Git repository. 

⚙️ Tech Stack
• Ultralytics
• Git + DagsHub
• DVC (Data Version Control)
• DagsHub Remote Storage

📦 Models currently tracked
• Bib Detector
• Box Detector
• CJ Model
• Jiju Model
• Seaseed Detector
• Tan Model

All models are tracked using DVC, which means:
✔ Model versioning
✔ Reproducibility
✔ Clean Git repositories
✔ Better collaboration for ML projects

💡 Sometimes the best learning happens when you just open a tool and explore what’s possible.

Fun fact 😄
I actually got introduced to DagsHub through a giveaway 2 years ago.

Check it out here:
🎁 https://lnkd.in/d4R56PSE

That small moment turned into a deeper interest in MLOps, experiment tracking and model management.

Now I'm exploring how to structure projects so they move beyond notebooks and become reproducible ML systems.

🚀 I’m open to:
• MLOps collaborations
• Computer vision research projects
• Open-source ML contributions
• Building production-ready AI pipelines

Let’s build and learn together.

Follow me Joel Nadar for more Computer Vision, MLOps experiments and real-world AI projects 👍🏾

Dean Pleban Glenn Jocher Muhammad Rizwan Munawar Guy Smoilovsky Paula Derrenger Simran Baiche Timothy Goebel Dragos Stan Arnaud Bastide

#MLOps #DagsHub #DVC #YOLO #Ultralytics #MachineLearning #ComputerVision #OpenSourceAI #AIEngineering #ModelVersioning #MLInfrastructure #DeepLearning #AIProjects #LearningInPublic

DagsHub

Ultralytics

Co-Founder & CEO at DagsHub | AI Data Development Platform

Founder & CEO at Ultralytics | Democratizing Vision AI 🚀

Computer vision | Content creator @ Ultralytics | LinkedIn Instructor | Building @ CollabX | 250K+ readers on medium.com | Open source contributor | YOLO26 | Vision language models

Co-Founder & CTO at DagsHub | AI Data Development Platform

VP of Growth @ Ultralytics | Democratizing vision AI 🚀

Aspiring Data Scientist | MSc Data Science Student | Machine Learning | Data Visualization | Power BI | Reinforcement Learning Enthusiast | Open to Opportunities

Founder & CEO, Ryza Content | AI Solutions Architect | Driving Consistent, Scalable Content with AI

Computer Vision Engineer

Computer Vision Solution Engineer | Intel® AI Software Innovator

YOLO Models Repository Management using DagsHub and DVC

A few months ago, I was working on a warehouse detection challenge identifying and tracking stacked boxes in dense logistics environments.

At that time, I was able to find a working solution, but I didn’t want to stop there.

Since then, I’ve been exploring this space deeper running multiple R&D experiments using Ultralytics models to understand how computer vision can solve real-world logistics problems more effectively 📦🤖
Warehouses and industrial sites are not clean lab environments.

They have:
• Dense object stacks
• Occlusions and overlaps
• Changing lighting conditions
• Constant movement
But with the right YOLO-based pipelines, these challenges can be handled reliably.

What excites me most is how these solutions can support:
🏭 Warehouse automation
📦 Inventory monitoring
🏗️ Industrial site safety
🚚 Logistics optimization

I’m actively experimenting with Ultralytics models and new vision pipelines to stay updated with the latest developments and build practical use cases that industries actually need.

For me, it’s not just about models  it’s about turning them into real solutions.

👉🏽 Follow me Joel Nadar for more real-world computer vision experiments, YOLO use cases and AI R&D insights.

Also open to collaborations, giveaways, meetups, research discussions and community learning opportunities 🤝🚀

Muhammad Rizwan Munawar Glenn Jocher Timothy Goebel Simran Baiche Arnaud Bastide 

#Ultralytics #YOLO #ComputerVision
#WarehouseAI #LogisticsAI #IndustrialAI
#ObjectDetection #VisionAI #DeepLearning
#AIEngineering #AppliedAI #AIProjects
#OpenToCollaborate #AICommunity #TechInnovation 🚀

Sometimes you just need to stand tall and grow differently. 🌴
Looking at this palm tree standing alone among many other trees reminds me of something important it’s okay to be different. While others grow in clusters, some trees grow higher, stronger and more visible because they choose their own direction.
In life and in our careers, the same applies.
Keep learning 📚, stay focused 🎯 and keep working on your craft 💻 even when your path looks different from everyone else.
One day, that difference becomes your strength. 💪🏽
Stay curious. Keep growing. And always do your best. 🚀

Joel Nadar Jacqueline Bhaskar 

#Motivation #KeepLearning #StayFocused #GrowthMindset #CareerJourney #NeverStopLearning #SelfImprovement #BeDifferent 🌱

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