Comparison of deep learning models on ImageNet 1K dataset: Parameters, accuracy, and inference time.

<p>Comparison of deep learning models on ImageNet 1K dataset: Parameters, accuracy, and inference time.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Ebrahim Parcham (20700216) (author)
مؤلفون آخرون:	Mansoor Fateh (8568711) (author), Vahid Abolghasemi (20700219) (author)
منشور في:	2025
الموضوعات:	Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified visual genome dataset original hybridbranchnet model imagenet 1k compared extensive evaluations demonstrate addressing critical limitations 6 %, respectively object relationship extraction complex classification tasks based methods allow adaptive feature extraction paper introduces hybridbranchnetv2 making hybridbranchnetv2 suitable analyze object relationships model improves adaptability enhance classification accuracy object relationships complex environments based techniques adaptive behavior world scenarios time applications reinforcement learning minimal risk key novelty improvement obtained computational efficiency comprehensive analysis approach lies algorithms struggle additional testing adapt effectively
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