cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/4zs-27j-sbh
title: Eugenia monticola (Sw.) DC.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/he0-xzp-v68
title: Ficus crocata (Miq.) Mart. ex Miq.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.15468/dl.zrsw5w
title: Occurrence Download

description: A dataset containing 309896 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is ABV - Common breeding birds in Flanders, Belgium"
 ]
}

 The dataset includes 309896 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000355-250710112747441/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/x7a-f64-v28
title: Feijoa sellowiana (O. Berg) O. Berg

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/lwx-0yz-00e
title: Mirabilis violacea (L.) Heimerl

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/nqg-l2f-ck7
title: Eugenia monticola (Sw.) DC.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/l3p-jrs-anp
title: Oxalis corniculata L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/zet-8v9-33c
title: Boerhavia erecta L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/apv-mm5-2vf
title: Oxalis galeottii Turcz.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/arw-cw7-gxa
title: Cissampelos pareira L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/706-8vl-kme
title: Cissampelos pareira L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/h9d-l4t-ejh
title: Cissampelos pareira L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/q7l-a6b-ts1
title: Mirabilis jalapa L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/153-7he-343
title: Oxalis frutescens subsp. angustifolia (Kunth) Lourteig

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/ehj-rwz-t75
title: Cissampelos pareira L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/0qr-chy-249
title: Cissampelos pareira L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/h2m-s2a-3xb
title: Oxalis frutescens subsp. angustifolia (Kunth) Lourteig

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/3jb-wx4-bdy
title: Disciphania spadicea Barneby

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/axe-4a6-tzl
title: Boerhavia erecta L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/rcg-e5p-q0n
title: Ficus maxima Mill.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/w4j-146-g2g
title: Myrcia calderonii (Standl.) A. R. Lourenço & Sánchez-Cháv.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/lz0-y6n-01t
title: Cupania guatemalensis (Turcz.) Radlk.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/7xl-fdy-b73
title: Piper pseudolindenii C. DC.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/yhr-pfm-9nq
title: Piper pseudofuligineum C. DC.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/qqc-463-sg7
title: Piper pseudolindenii C. DC.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/blw-1rr-dlj
title: Ardisia paschalis Donn. Sm.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/dph-mdk-p84
title: Ardisia compressa Kunth

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/81s-jpw-2x4
title: Miconia guatemalensis Cogn. ex Donn. Sm.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/j38-d87-7l0
title: Psidium guajava L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/8y9-8zy-1vw
title: Ardisia compressa Kunth

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/dhg-x02-w6l
title: Psidium guajava L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.15468/dl.2224mu
title: Occurrence Download

description: A dataset containing 27379 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Aspidoscelis gularis"
 ]
}

 The dataset includes 27379 records from 95 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000359-250710112747441/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g9w27b
title: Occurrence Download

description: A dataset containing 3524 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Chlidonias niger)",
 "Country is one of (Afghanistan, Armenia, Azerbaijan, Bahrain, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Cyprus, Georgia, Hong Kong, India, Indonesia, Iran (Islamic Republic of), Iraq, Israel, Japan, Jordan, Kazakhstan, Korea (Democratic People’s Republic of), Korea, Republic of, Kuwait, Kyrgyzstan, Lao People’s Democratic Republic, Lebanon, Macao, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Oman, Pakistan, Palestine, State of, Philippines, Qatar, Saudi Arabia, Singapore, Sri Lanka, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Türkiye, Turkmenistan, United Arab Emirates, Uzbekistan, Viet Nam, Yemen)"
 ]
}

 The dataset includes 3524 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041656-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zddtcp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Alexandrium tamarense",
 "Country is Chile",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041657-250717081556266/datasets/export for details.
DOI: 10.15468/dl.srhuwz
title: Occurrence Download

description: A dataset containing 53 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lepidium didymum",
 "Country is Israel",
 "HasCoordinate is true"
 ]
}

 The dataset includes 53 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041658-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2dzqdw
title: Occurrence Download

description: A dataset containing 253 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lepidium didymum",
 "Country is Italy",
 "HasCoordinate is true"
 ]
}

 The dataset includes 253 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041660-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z3zewv
title: Occurrence Download

description: A dataset containing 78 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mimosa pigra",
 "Country is Viet Nam",
 "HasCoordinate is true"
 ]
}

 The dataset includes 78 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041659-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7zy4nr
title: Occurrence Download

description: A dataset containing 2497 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Choeronycteris mexicana"
 ]
}

 The dataset includes 2497 records from 95 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041661-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6nuk67
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Alexandrium tamarense",
 "Country is Denmark",
 "HasCoordinate is true"
 ]
}

 The dataset includes 8 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041662-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ajhwdn
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Knodus septentrionalis)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 24 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041663-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ryfb4j
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dysphania botrys",
 "Country is Türkiye",
 "HasCoordinate is true"
 ]
}

 The dataset includes 15 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041665-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m7yvnb
title: Occurrence Download

description: A dataset containing 100 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lonchophylla mordax"
 ]
}

 The dataset includes 100 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041666-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hftg5p
title: Occurrence Download

description: A dataset containing 100 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lonchophylla mordax"
 ]
}

 The dataset includes 100 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041667-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16415620
title: Table 2 in An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images

description: Table 2 Performance comparison of the proposed YOLO model with those reported other published works
AuthorsTechniquesDatasets/Blood smearsPlasmodium parasitesmAP (%)Yang F et al. [30]Modified YOLOv2Self-collected 2567 thin blood smear imagesP.vivax79.22Koirala.et al. [33]Modified YOLOv3 and YOLOV4 TinySelf-collected 3885 thick blood smear imagesP.falciparum94.07Sukumarran et al. [32]YOLOv4MP-IDB and 236 images from MRC-UNIMAS SarawakP.falciparum P.vivax84.04P.ovaleP.malariaeP.knowlesiAbdurahman et al. [62]Modified YOLOv3 and YOLOv4Publicly available 1182 thick blood smear imagesP.falcipraum89.73Present study (YOLOv4-RC3_4)Modified YOLOv4MP-IDB The malaria parasite image database and new dataset from MRC-UNIMAS SarawakP.falciparum90.07P.vivaxP.ovaleP.malariaeP.knowlesi
mAP Mean average precision, MP-IDB Malaria Parasite Image Database, MRC-UNIMAS Malaria Research Centre-Universiti Malaysia Sarawak, YOLO You Only Look Once object detection algorithm
description: Published as part of Sukumarran, Dhevisha, Hasikin, Khairunnisa, Khairuddin, Anis Salwa Mohd, Ngui, Romano, Sulaiman, Wan Yusoff Wan, Vythilingam, Indra & Divis, Paul Cliff Simon, 2024, An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images, pp. 8 in Parasites & Vectors (188) 17 (1) on page 8, DOI: 10.1186/s13071-024-06215-7, http://zenodo.org/record/11074830
DOI: 10.5281/zenodo.16415621
title: Table 2 in An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images

description: Table 2 Performance comparison of the proposed YOLO model with those reported other published works
AuthorsTechniquesDatasets/Blood smearsPlasmodium parasitesmAP (%)Yang F et al. [30]Modified YOLOv2Self-collected 2567 thin blood smear imagesP.vivax79.22Koirala.et al. [33]Modified YOLOv3 and YOLOV4 TinySelf-collected 3885 thick blood smear imagesP.falciparum94.07Sukumarran et al. [32]YOLOv4MP-IDB and 236 images from MRC-UNIMAS SarawakP.falciparum P.vivax84.04P.ovaleP.malariaeP.knowlesiAbdurahman et al. [62]Modified YOLOv3 and YOLOv4Publicly available 1182 thick blood smear imagesP.falcipraum89.73Present study (YOLOv4-RC3_4)Modified YOLOv4MP-IDB The malaria parasite image database and new dataset from MRC-UNIMAS SarawakP.falciparum90.07P.vivaxP.ovaleP.malariaeP.knowlesi
mAP Mean average precision, MP-IDB Malaria Parasite Image Database, MRC-UNIMAS Malaria Research Centre-Universiti Malaysia Sarawak, YOLO You Only Look Once object detection algorithm
description: Published as part of Sukumarran, Dhevisha, Hasikin, Khairunnisa, Khairuddin, Anis Salwa Mohd, Ngui, Romano, Sulaiman, Wan Yusoff Wan, Vythilingam, Indra & Divis, Paul Cliff Simon, 2024, An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images, pp. 8 in Parasites & Vectors (188) 17 (1) on page 8, DOI: 10.1186/s13071-024-06215-7, http://zenodo.org/record/11074830
DOI: 10.5281/zenodo.16415623
title: Table 3 in An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images

description: Table 3 Data distribution for training and testing the model
DatabaseTotal number of images beforeTraining imagesNumberaugmentationNumber of training images before augmentationNumber of training images after augmentationof testing images aMP-IDB (Dataset A)210168 (80%)100042 (20%)Malaria Research Centre, UNIMAS (Dataset B)472––472
MP-IDB Malaria Parasite Image Database, UNIMAS Universiti Malaysia Sarawak a No augmentation was performed on testing images
description: Published as part of Sukumarran, Dhevisha, Hasikin, Khairunnisa, Khairuddin, Anis Salwa Mohd, Ngui, Romano, Sulaiman, Wan Yusoff Wan, Vythilingam, Indra & Divis, Paul Cliff Simon, 2024, An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images, pp. 10 in Parasites & Vectors (188) 17 (1) on page 10, DOI: 10.1186/s13071-024-06215-7, http://zenodo.org/record/11074830
DOI: 10.5281/zenodo.16415622
title: Table 3 in An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images

description: Table 3 Data distribution for training and testing the model
DatabaseTotal number of images beforeTraining imagesNumberaugmentationNumber of training images before augmentationNumber of training images after augmentationof testing images aMP-IDB (Dataset A)210168 (80%)100042 (20%)Malaria Research Centre, UNIMAS (Dataset B)472––472
MP-IDB Malaria Parasite Image Database, UNIMAS Universiti Malaysia Sarawak a No augmentation was performed on testing images
description: Published as part of Sukumarran, Dhevisha, Hasikin, Khairunnisa, Khairuddin, Anis Salwa Mohd, Ngui, Romano, Sulaiman, Wan Yusoff Wan, Vythilingam, Indra & Divis, Paul Cliff Simon, 2024, An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images, pp. 10 in Parasites & Vectors (188) 17 (1) on page 10, DOI: 10.1186/s13071-024-06215-7, http://zenodo.org/record/11074830
DOI: 10.5281/zenodo.16415624
title: Table 4 in An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images

description: Table 4 Network structure of YOLOv4. Network structure of the YOLOv4 model
YOLOv4 melsNumber of layersTypeCSPDarkNet53 without the fully connected layer0 1–7 8CBL Res Conv9Route10–202Res21Conv22Route23–518Res52Conv53Route54–828Res83Conv84Route85–1014Res102Conv103RouteFeature fusion layer and output layer104–107 108–1134CBL SPP114–1174CBL118Up-sample119Route120Conv121Route122–1276CBL128Up-sample129Route130CBL131Route132–1376CBL138–139CBL +YOLO140Route141CBL142Route143–1486CBL149–150Conv +YOLO151Route152CBL153Route154–1596CBL160–161Conv +YOLO
Head:The main function is to locate the bounding boxes and classify the objects of interest.The coordinates and the scores of every bounding box are generated YOLO You Only Look Once (model), CBL Convolutional,Batch normalisation,and Leaky-ReLU (Feature extractor), RES residual block
description: Published as part of Sukumarran, Dhevisha, Hasikin, Khairunnisa, Khairuddin, Anis Salwa Mohd, Ngui, Romano, Sulaiman, Wan Yusoff Wan, Vythilingam, Indra & Divis, Paul Cliff Simon, 2024, An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images, pp. 12 in Parasites & Vectors (188) 17 (1) on page 12, DOI: 10.1186/s13071-024-06215-7, http://zenodo.org/record/11074830
DOI: 10.5281/zenodo.16415625
title: Table 4 in An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images

description: Table 4 Network structure of YOLOv4. Network structure of the YOLOv4 model
YOLOv4 melsNumber of layersTypeCSPDarkNet53 without the fully connected layer0 1–7 8CBL Res Conv9Route10–202Res21Conv22Route23–518Res52Conv53Route54–828Res83Conv84Route85–1014Res102Conv103RouteFeature fusion layer and output layer104–107 108–1134CBL SPP114–1174CBL118Up-sample119Route120Conv121Route122–1276CBL128Up-sample129Route130CBL131Route132–1376CBL138–139CBL +YOLO140Route141CBL142Route143–1486CBL149–150Conv +YOLO151Route152CBL153Route154–1596CBL160–161Conv +YOLO
Head:The main function is to locate the bounding boxes and classify the objects of interest.The coordinates and the scores of every bounding box are generated YOLO You Only Look Once (model), CBL Convolutional,Batch normalisation,and Leaky-ReLU (Feature extractor), RES residual block
description: Published as part of Sukumarran, Dhevisha, Hasikin, Khairunnisa, Khairuddin, Anis Salwa Mohd, Ngui, Romano, Sulaiman, Wan Yusoff Wan, Vythilingam, Indra & Divis, Paul Cliff Simon, 2024, An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images, pp. 12 in Parasites & Vectors (188) 17 (1) on page 12, DOI: 10.1186/s13071-024-06215-7, http://zenodo.org/record/11074830
DOI: 10.5281/zenodo.16415628
title: Table 5 in An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images

description: 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)
description: Published as part of Sukumarran, Dhevisha, Hasikin, Khairunnisa, Khairuddin, Anis Salwa Mohd, Ngui, Romano, Sulaiman, Wan Yusoff Wan, Vythilingam, Indra & Divis, Paul Cliff Simon, 2024, An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images, pp. 13 in Parasites & Vectors (188) 17 (1) on page 13, DOI: 10.1186/s13071-024-06215-7, http://zenodo.org/record/11074830
DOI: 10.5281/zenodo.16415627
title: Table 5 in An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images

description: 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)
description: Published as part of Sukumarran, Dhevisha, Hasikin, Khairunnisa, Khairuddin, Anis Salwa Mohd, Ngui, Romano, Sulaiman, Wan Yusoff Wan, Vythilingam, Indra & Divis, Paul Cliff Simon, 2024, An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images, pp. 13 in Parasites & Vectors (188) 17 (1) on page 13, DOI: 10.1186/s13071-024-06215-7, http://zenodo.org/record/11074830
DOI: 10.5281/zenodo.16415629
title: Table 6 Residual block removal from the C 3, C 4 and C 5 in An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images

description: Table 6 Residual block removal from the C3, C4 and C5 layers of the CSPDarknet53 backbone
ModificationsModelsRes-block bodiesResidual blocks (n)Before AfterResidual blocks pruningYOLOv4-RC3C38 3YOLOv4-RC4C48 3YOLOv4-RC5C54 2YOLOv4-RC3_4 C3 and C48, 8 3, 3YOLOv4-RC3_5 C3 and C58, 4 3, 2
YOLO You Only Look Once (model)
description: Published as part of Sukumarran, Dhevisha, Hasikin, Khairunnisa, Khairuddin, Anis Salwa Mohd, Ngui, Romano, Sulaiman, Wan Yusoff Wan, Vythilingam, Indra & Divis, Paul Cliff Simon, 2024, An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images, pp. 13 in Parasites & Vectors (188) 17 (1) on page 13, DOI: 10.1186/s13071-024-06215-7, http://zenodo.org/record/11074830