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    <identifier>10.57760/sciencedb.14338</identifier>
    <datestamp>2024-10-08T15:38:41Z</datestamp>
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  <dc:date>2024-10-08</dc:date>
  <dc:title>Dataset and Code for Fine-tuning Neural Networks via Transfer Learning on Image Classification Tasks</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.14338</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>This dataset contains results from experiments where transfer learning was applied to neural network models pre-trained on the ImageNet dataset. The final fully connected layer of 1000 units was replaced with an 8-unit layer to fit the specific classification tasks in this study. The models were fine-tuned with a custom dataset to optimize performance for categorization, while core layers remained unchanged. Fine-tuning was conducted across the network based on the configurations of pre-existing model parameters. This process aimed at enhancing the model's adaptability for specialized tasks.</dc:description>
  <dc:subject>neural network; transfer learning; ImageNet</dc:subject>
  <dc:creator>Rolly R. Tang</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/publicdomain/zero/1.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:publisher>Science Data Bank</dc:publisher>
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