Learn how Encord Annotate can help you level up your label operations and build better models. In the tutorials you will learn how to automate your labeling, work with different modalities, and much more.

Advanced

4 Minutes

How to use SAM to Automate Data Labeling

A short tutorial of how to use SAM to automate your labeling in Encord Annotate.

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Advanced

11 Minutes

How to use the Annotator Training Module

Learn how to use the Annotator Training Module in Encord Annotate to improve the performance and test the quality of your annotators.

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Advanced

6 Minutes

How to Attach Your Cloud Bucket with the Web App

A tutorial on attaching creating an S3 integration with Encord.

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Advanced

4 Minutes

How to use Interpolation

Learn how to predict annotations between frames with interpolation.

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Getting Started with Workflows

Learn how to set up and maximise value from Workflows in the Encord platform.

Webinars & Events

Rewatch Encord's webinars, conference speeches, and product demos on-demand.

Webinars & Events

57 Minutes

How to Fine Tune Foundation Models to Auto-Label Training Data

Foundation models, like Meta’s Segment Anything Model (SAM), have provided a host of benefits for data and ML teams looking to expedite the production of training data whilst improving the quality. This webinar walks you through how to go one step further and fine-tune foundation models, in particular Meta AI's SAM, to maximize relevance to your specific use case.

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Webinars & Events

59 Minutes

How to build Semantic Visual Search with ChatGPT and CLIP

OpenAI’s ChatGPT and CLIP releases have revolutionised the ways in which organisations and individual contributors can ship features to their users. At Encord, we’ve focused on how the neural network (CLIP) and LLM (ChatGPT) can be combined to build an effective and powerful Semantic Visual Search. Frederik Hvilshøj, Lead ML Engineer with a PhD in Generative AI, joins Eric Landau, CEO and Co-Founder of Encord, to provide actionable insights into how to build this function from scratch.

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Webinars & Events

55 Minutes

AI for Augmented Reality in 2023 and Beyond

This edition of Encord’s Fireside Chats sees Victor Prisacariu, of the University of Oxford and Niantic, sit down with Eric Landau, Encord’s CEO and Co-Founder, to discuss recent developments in AI computer vision and machine learning. Victor’s focus currently lies in real-time Augmented Reality on mobile and wearable platforms, having co-founded 6D.ai which was later acquired by Niantic in March 2020. With his wide and varied experience of the industry, Victor touched crucial areas of AR as well as discussing his work at Niantic in-depth.

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Webinars & Events

47 Minutes

Are VFMs on par with SOTA?

With Foundational Models increasing in prominence, Encord's President and Co-Founder sat down with our Lead ML Engineer to dissect Meta's new Visual Foundation Model: Segment Anything (SAM). After combining the model with Grounding-DINO to allow for zero-shot segmentation, they compared it to a SOTA Mask-RCNN model to see whether the development of SAM really is revolutionary for segmentation.

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Webinars & Events

47 Minutes

Using ChatGPT to Improve a Computer Vision Model

With the proliferation of use cases for ChatGPT, we set out to investigate whether ChatGPT could be used to make improvements in other AI systems. We tested it on a practical problem in a modality of AI in which it has not been trained on - computer vision - and reported the results. ChatGPT's suggestions achieved on average a 10.1% improvement in precision and a 34.4% improvement in recall over our random sample, using a purely data-centric metric-driven approach. Eric Landau, Encord's Co-founder and CEO, sits down with the Data-Centric AI Community to share more about the process & lessons learned!

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Webinars & Events

58 Minutes

Fireside with Luc Vincent, VP of AI at Meta

Encord's CEO & Co-founder, Eric Landau, sat down with Luc Vincent, VP of AI at Meta & also Executive Advisor at Encord - as he took us through some of the lessons learned from his career, from building Lyft's first autonomous vehicle organization, to Google's geo imagery division, and now the metaverse at Meta. Luc also touched on: the challenges and lessons learned from setting up & scaling world-class computer vision orgs that are pushing the boundaries of AI, the working environment and culture in the early days of Google, the ML applications and promising projects he's most excited about going into the next decade. And what it'll take for the winners to get ahead!

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Papers explained

Read detailed explainers of cutting edge research papers we like at Encord and implement in our platform.

sampleImage_imagebind-embedding-model-explained

Machine Learning

ImageBind MultiJoint Embedding Model from Meta Explained

sampleImage_dinov2-self-supervised-learning-explained

Machine Learning

DINOv2: Self-supervised Learning Model Explained

sampleImage_seggpt-segment-everything-in-context-explainer

Machine Learning

SegGPT: Segmenting everything in context [Explained]

sampleImage_segment-anything-model-explained

Machine Learning

Meta AI's Segment Anything Model (SAM) Explained: The Ultimate Guide

The Only Platform That Wants You To Label Less

Learn how Encord's platform can help you streamline your computer vision data operations through simple video tutorials and beginner courses.