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What’s New in PyTorch 2.0? torch.compile

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Project Structure Accelerating Convolutional Neural Networks Parsing Command Line Arguments and Running a Model Evaluating Convolutional Neural Networks Accelerating Vision Transformers Evaluating Vision Transformers Accelerating BERT Evaluating BERT Miscellaneous Summary Citation Information What’s New in PyTorch 2.0?

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Faster R-CNNs

PyImageSearch

Home Table of Contents Faster R-CNNs Object Detection and Deep Learning Measuring Object Detector Performance From Where Do the Ground-Truth Examples Come? One of the most popular deep learning-based object detection algorithms is the family of R-CNN algorithms, originally introduced by Girshick et al.

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Setting Up a Training, Fine-Tuning, and Inferencing of LLMs with NVIDIA GPUs and CUDA

Unite.AI

The Rise of CUDA-Accelerated AI Frameworks GPU-accelerated deep learning has been fueled by the development of popular AI frameworks that leverage CUDA for efficient computation. NVIDIA TensorRT , a high-performance deep learning inference optimizer and runtime, plays a vital role in accelerating LLM inference on CUDA-enabled GPUs.

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Image Recognition: The Basics and Use Cases (2024 Guide)

Viso.ai

Image recognition with deep learning is a key application of AI vision and is used to power a wide range of real-world use cases today. I n past years, machine learning, in particular deep learning technology , has achieved big successes in many computer vision and image understanding tasks.

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Classifying Rice With PyTorch: A Step-by-Step Guide

Towards AI

But how can we harness machine learning for something as niche as rice classification? Well, this is where PyTorch, a powerful deep learning library, steps in. Today, I’ll guide you through creating a Convolutional Neural Network (CNN) using PyTorch to classify rice varieties based on images.

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Deploying a Vision Transformer Deep Learning Model with FastAPI in Python

PyImageSearch

Home Table of Contents Deploying a Vision Transformer Deep Learning Model with FastAPI in Python What Is FastAPI? You’ll learn how to structure your project for efficient model serving, implement robust testing strategies with PyTest, and manage dependencies to ensure a smooth deployment process. Testing main.py Testing main.py

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Training a Custom Image Classification Network for OAK-D

PyImageSearch

Furthermore, this tutorial aims to develop an image classification model that can learn to classify one of the 15 vegetables (e.g., If you are a regular PyImageSearch reader and have even basic knowledge of Deep Learning in Computer Vision, then this tutorial should be easy to understand. tomato, brinjal, and bottle gourd).