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Benchmarking Computer Vision Models using PyTorch & Comet

Heartbeat

[link] Transfer learning using pre-trained computer vision models has become essential in modern computer vision applications. In this article, we will explore the process of fine-tuning computer vision models using PyTorch and monitoring the results using Comet. Pre-trained models, such as VGG, ResNet.

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Managing Computer Vision Projects with Micha? Tadeusiak 

The MLOps Blog

Every episode is focused on one specific ML topic, and during this one, we talked to Michal Tadeusiak about managing computer vision projects. I’m joined by my co-host, Stephen, and with us today, we have Michal Tadeusiak , who will be answering questions about managing computer vision projects.

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Training a Custom Image Classification Network for OAK-D

PyImageSearch

Table of Contents Training a Custom Image Classification Network for OAK-D Configuring Your Development Environment Having Problems Configuring Your Development Environment? Furthermore, this tutorial aims to develop an image classification model that can learn to classify one of the 15 vegetables (e.g.,

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This AI Paper Unveils X-Raydar: A Groundbreaking Open-Source Deep Neural Networks for Chest X-Ray Abnormality Detection

Marktechpost

The X-Raydar achieved a mean AUC of 0.919 on the auto-labeled set, 0.864 on the consensus set, and 0.842 on the MIMIC-CXR test. X-Raydar, the computer vision algorithm, used InceptionV3 for feature extraction and achieved optimal results using a custom loss function and class weighting factors.

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How to Use Hugging Face Pipelines?

Towards AI

Hugging Face is a platform that provides pre-trained language models for NLP tasks such as text classification, sentiment analysis, and more. The NLP tasks we’ll cover are text classification, named entity recognition, question answering, and text generation. The pipeline we’re going to talk about now is zero-hit classification.

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TinyML: Applications, Limitations, and It’s Use in IoT & Edge Devices

Unite.AI

Vision Based Applications TinyML has the potential to play a crucial role in processing computer vision based datasets because for faster outputs, these data sets need to be processed on the edge platform itself. The results obtained from the setup were accurate, the design was low-cost, and it delivered satisfactory results.

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Building and Deploying CV Models: Lessons Learned From Computer Vision Engineer

The MLOps Blog

With over 3 years of experience in designing, building, and deploying computer vision (CV) models , I’ve realized people don’t focus enough on crucial aspects of building and deploying such complex systems. Hopefully, at the end of this blog, you will know a bit more about finding your way around computer vision projects.