Unverified50% confidenceConceptExact time
Precision, recall, F1 score, and accuracy can all be derived from the four numbers of the confusion matrix.
1
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50%
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Long-term
Relevance
10/19/2026
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VerifiedThe F1 score is the harmonic mean of recall and precision.74% similarUnverifiedMixed-precision quantization applies aggressive low precision such as INT4 to backbone networks that have large parameter counts but are not precision-sensitive, while retaining INT8 or FP16 for precision-sensitive components such as text encoders and QKV projections.68% similarUnverifiedThe combination of EfficientNet-B3 and SVM achieved the strongest baseline performance, with an accuracy of 96.31% and a macro F1 score of 96.36%.68% similarUnverifiedRegardless of the encoding scheme used, 4-bit precision can only distinguish 16 different numerical levels.67% similarVerifiedThe core principle of quantization is to convert model parameters from high-precision floating-point representations (such as FP32's 32-bit floating point) to low-precision representations (such as INT8's 8-bit integer or INT4's 4-bit integer).67% similar
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