[Evaluation indicators] FLOPs, MACs (Multiply–Accumulate Operations), Params - 코드 세계

[Evaluation indicators] FLOPs, MACs (Multiply–Accumulate Operations), Params

발 2023-09-11 18:10:36 독서 시간: null

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[Evaluation indicators] FLOPs, MACs (Multiply–Accumulate Operations), Params

[Evaluation indicators] FLOPs, MACs (Multiply–Accumulate Operations), Params

[Evaluation indicators] FLOPs, MACs (Multiply–Accumulate Operations), Params

In recommendation systems, commonly used evaluation indicators are as follows:

Image evaluation: Calculate PSNR, SSIM, MSE, LPIPS and NIQE evaluation indicators

Master the performance evaluation indicators and examples of operation and maintenance in one article

Understand the evaluation indicators NDCG (CG, DCG, IDCG) in the recommendation system

Multiple Object Tracking (MOT, Multiple Object Tracking) evaluation indicators

FLOPS、FLOPs、FPS

Relationship between MACs (number of multiplication and addition operations) and FLOPs (number of floating point operations), onnx function calculation method

Commonly used evaluation indicators for knowledge maps: MRR, MR, HITS@K, Recall@K, Precision@K

计算深度学习模型的macs

R language uses the train function of the caret package to build a regularized generalized linear model (glmnet) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build a Bagging AdaBoost model (Bagged AdaBoost) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build a penalty discriminant analysis model (pda) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build an xgboost model (based on the gbtree algorithm) model to build a classification model, trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

hbctraining-05_peak_calling_macs2

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HOLLiAS MACS-K 시스템 하드웨어 개요

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CONDITIONS EVALUATION REPORT

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