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  Published Paper Details:

  Paper Title

OBJECT DETECTION USING RESNET50

  Authors

  Mr. Gangadhar Doma,  Mrs. Tulasi Miriyala

  Keywords

CNN. ResNet 50, ResNet, Object Detection, COCO Dataset.

  Abstract


Object identification is a central problem in computer vision to detect and locate objects of interest in photos and movies. CNNs, a class of deep learning models, have recently revolutionized the field of object identification with their incredible accuracy. ResNet50 has proven to be a powerful framework for identifying object tasks for these models.The object identification studies in this publication use the ResNet50 model. The ResNet50 architecture is a modification of the ResNet model that uses hopping connections to overcome the degradation problem of deep-connection neural networks. ResNet50 solves the vanishing gradient problem by providing residual connections that allow training of deeper networks while preserving improved gradient flow. The goal of this project is to explore ResNet50's ability to locate and recognize elements in complex environments. The collection of photos used for evaluation includes different scales, occlusions, and object classifications. A large labeled COCO dataset with annotation bounds for each instance of each element is used to train the ResNet50 model.This study also explores how model settings and hyperparameter values affect the effectiveness of ResNet50. Investigate how to optimize your model, speed up inference, and improve computational efficiency.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2401870

  Paper ID - 263013

  Page Number(s) - h385-h394

  Pubished in - Volume 12 | Issue 1 | January 2024

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Mr. Gangadhar Doma,  Mrs. Tulasi Miriyala,   "OBJECT DETECTION USING RESNET50", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 1, pp.h385-h394, January 2024, Available at :http://www.ijcrt.org/papers/IJCRT2401870.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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