Tenyks

Multimodal Large Language Models: Transforming Computer Vision

This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. This article introduces multimodal large language models (MLLMs) [1], their applications using challenging prompts, and the top models reshaping computer vision as we speak. What is a multimodal large language model (MLLM)? In layman terms, a multimodal […]

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DALL-E vs Gemini vs Stability: GenAI Evaluations

This article was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. We performed a side-by-side comparison of three models from leading providers in Generative AI for Vision. This is what we found: Despite the subjectivity involved in Human Evaluation, this is the best approach to evaluate state-of-the-art GenAI Vision

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Improving Vision Model Performance Using Roboflow and Tenyks

This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. When improving an object detection model, many engineers focus solely on tweaking the model architecture and hyperparameters. However, the root cause of mediocre performance often lies in the data itself. ‍In this collaborative post between Roboflow and

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NVIDIA TAO Toolkit: How to Build a Data-centric Pipeline to Improve Model Performance  (Part 3 of 3)

This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. During this series, we will use Tenyks to build a data-centric pipeline to debug and fix a model trained with the NVIDIA TAO Toolkit. ‍Part 1. We demystify the NVIDIA ecosystem and define a data-centric pipeline based

NVIDIA TAO Toolkit: How to Build a Data-centric Pipeline to Improve Model Performance  (Part 3 of 3) Read More +

NVIDIA TAO Toolkit: How to Build a Data-centric Pipeline to Improve Model Performance  (Part 2 of 3)

This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. During this series, we will use Tenyks to build a data-centric pipeline to debug and fix a model trained with the NVIDIA TAO Toolkit. Part 1. We demystify the NVIDIA ecosystem and define a data-centric pipeline based

NVIDIA TAO Toolkit: How to Build a Data-centric Pipeline to Improve Model Performance  (Part 2 of 3) Read More +

NVIDIA TAO Toolkit: How to Build a Data-centric Pipeline to Improve Model Performance  (Part 1 of 3)

This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. In this series, we’ll build a data-centric pipeline using Tenyks to debug and fix a model trained with the NVIDIA TAO Toolkit. ‍Part 1. We demystify the NVIDIA ecosystem and define a data-centric pipeline tailored for a

NVIDIA TAO Toolkit: How to Build a Data-centric Pipeline to Improve Model Performance  (Part 1 of 3) Read More +

Computer Vision Pipeline v2.0

This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. In the realm of computer vision, a shift is underway. This article explores the transformative power of foundation models, digging into their role in reshaping the entire computer vision pipeline. ‍It also demystifies the hype behind the

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Amid the Rise of LLMs, is Computer Vision Dead?

This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. The field of computer vision has seen incredible progress, but some believe there are signs it is stalling. At the International Conference on Computer Vision 2023 workshop “Quo Vadis, Computer Vision?”, researchers discussed what’s next for computer

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Becoming a Computer Vision Engineer

This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. In the journey to become a proficient computer vision engineer, mastering the skills required at each stage of the machine learning life-cycle is crucial. This article introduces a blueprint with the skills a computer vision engineer is

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The Unseen Cost of Low Quality Large Datasets

This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. Your current data selection process may be limiting your models. ‍Massive datasets come with obvious storage and compute costs. But the two biggest challenges are often hidden: Money and Time. With increasing data volumes, companies have a

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Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.

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