https://www.selleckchem.com/products/wz4003.html
Various DCNNs had their architectures truncated, which retained only their initial core block, reducing their parameter sizes to less then 1 M. Once trained and validated, findings have shown that a DCNN with robust layer aggregations like the InceptionResNetV2 had less vulnerability to the adverse effects of the proposed truncation. The results also showed that from its full-length size of 55 M with 98.67% accuracy, the proposed truncation reduced its parameters to only 441 K and still attained an accuracy of 97.41%, outperforming other
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