Table 5 in An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images

Table 5 Network architecture of the pruned models Candidate modelRes-block 3 (C3)Res-block 4 (C4)Res-block 5 (C5)1 ×1/1 3 ×3/1Candidate 1’x’‘/’‘/’(YOLOv4-RC3)[52 ×52 ×128][26 ×26 256][13 × 13× 512]Candidate 2‘/’’x’‘/’(YOLOv4-RC4)[52 ×52 ×128][26 ×26 256][13 × 13× 512]Candidate 3‘/’‘/’’x’(YOLOv4-RC5)[52 ×52 ×128][52 ×52 ×128][13 × 13× 512]Candidate 4’x’’x’‘/’(YOLOv4-RC3_4)[52 ×52× 128][26 ×26 256][13 × 13× 512]Candidate 5’x’‘/’’x’(YOLOv4-RC3_5)[52 ×52× 128][26 ×26 256][13 × 13×512] ’x’ indicates that pruning occurs on the Res-Block bodies;‘/’ indicates that no pruning occurs on the Res-block bodies;values in square brackets indicate the image output sizes on the remaining layers of the Res-block bodies YOLO You Only Look Once (model)