PyImageSearch

NOVEMBER 13, 2023

You’ll typically find IoU and mAP used to evaluate the performance of HOG + Linear SVM detectors ( Dalal and Triggs, 2005 ), Convolutional Neural Network methods, such as Faster R-CNN ( Girshick et al., For more information, including a worked example of how to compute mAP, please see Hui (2018). 2015 ; He et al.,

Convolutional Neural Networks 119

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 119

Viso.ai

DECEMBER 3, 2023

Hence, rapid development in deep convolutional neural networks (CNN) and GPU’s enhanced computing power are the main drivers behind the great advancement of computer vision based object detection. Various two-stage detectors include region convolutional neural network (RCNN), with evolutions Faster R-CNN or Mask R-CNN.

Convolutional Neural Networks 98

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 98

Viso.ai

NOVEMBER 4, 2024

2018) applied STSs to determine the optimal path the solar collector follows the Sun. Computer Vision Model for Solar Prediction The researchers based their solution on computer vision, specifically deep Convolutional neural networks (CNNs) for object localization and identification. Carballo et al.

Computer Vision 52

Computer Vision Convolutional Neural Networks Neural Network Deep Learning 52

Viso.ai

NOVEMBER 5, 2024

YOLOv3 Darknet53 in YOLOv3 – source YOLOv3 was released in 2018 and introduced a deeper backbone network, the Darknet-53, which had 53 convolutional layers. This deeper network helped with better feature extraction. They categorized these experiments as Bag of Freebies (BoF) and Bag of Specials (BoS).

Convolutional Neural Networks 98

Convolutional Neural Networks Neural Network Categorization Automation 98

Viso.ai

DECEMBER 18, 2023

YOLO’s architecture was a significant revolution in the real-time object detection space, surpassing its predecessor – the Region-based Convolutional Neural Network (R-CNN). The backbone is a pre-trained Convolutional Neural Network (CNN) that extracts low, medium, and high-level feature maps from an input image.

Computer Vision 52

Computer Vision Convolutional Neural Networks Neural Network Algorithm 52

Snorkel AI

MARCH 1, 2023

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al. 2016) This paper introduced DCGANs, a type of generative model that uses convolutional neural networks to generate images with high fidelity. Attention Is All You Need Vaswani et al.

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

Unite.AI

AUGUST 8, 2023

Instead of complex and sequential architectures like Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs), the Transformer model introduced the concept of attention, which essentially meant focusing on different parts of the input text depending on the context.

Generative AI 243

Generative AI ChatGPT Neural Network Convolutional Neural Networks 243