{"data":{"id":"10.5061/dryad.1c59zw48r","type":"dois","attributes":{"doi":"10.5061/dryad.1c59zw48r","prefix":"10.5061","suffix":"dryad.1c59zw48r","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Longfei, Gao","nameType":"Personal","givenName":"Gao","familyName":"Longfei","affiliation":["Beijing Technology and Business University"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0007-1261-0227","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"TMC-Tongue: A standardized tongue image dataset with pathological annotations for AI-assisted TCM diagnosis"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"subject":"FOS: Basic medicine","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Basic medicine","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Medical and health sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Medical and health sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Target detection","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Traditional Chinese medicine","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Tongue","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2025-09-23T03:25:17Z","dateType":"Created"},{"date":"2025-09-23T03:29:40Z","dateType":"Submitted"},{"date":"2026-01-06T00:00:00Z","dateType":"Issued"},{"date":"2026-01-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementedBy","relatedIdentifier":"https://github.com/m28805746-max/Intelligent-tongue-diagnosis-detection-dataset","relatedIdentifierType":"URL"},{"relationType":"IsCitedBy","relatedIdentifier":"10.62762/bish.2026.303296","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["2343410168 bytes"],"formats":[],"version":"5","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This dataset contains 21 disease categories that can be used for target\n detection in tongue diagnosis. The categories are jiankangshe (Healthy\n Tongue), botaishe (Tongue with peeling coating), hongshe (Red tongue),\n zishe (Purple tongue), pangdashe (Chubby tongue), shoushe (Thin tongue),\n hongdianshe (Red dot tongue), liewenshe (Cracked tongue), chihenshe\n (Dentate tongue), baitaishe (White coating tongue), huangtaishe (Yellow\n coating tongue), heitaishe (Black coating tongue), huataishe (Smooth\n coating tongue), shenquao (renal depression), shenqutu (renal protrusion),\n gandanao (Hepatobiliary depression), gandantu (Hepatobiliary protrusion),\n piweiao (spleen and stomach depression), xinfeitu (heart and lung\n protrusion), xinfeiao (heart and lung depression), corresponding numerical\n order is 0-19. Among them, there are 5594 images in the training set, 572\n images in the validation set, and 553 images in the test set. Contains\n three annotation formats: coco/. txt/. XML, which can be used for\n experiments using relevant object detection algorithms through\n configuration files.","descriptionType":"Abstract"},{"description":"# TMC-Tongue: A standardized tongue image dataset with pathological\n annotations for AI-assisted TCM diagnosis Dataset DOI:\n [10.5061/dryad.1c59zw48r](10.5061/dryad.1c59zw48r) ## Description of the\n data and file structure Using high-precision cameras to capture\n volunteers' tongues to obtain data, all data only includes\n volunteers' tongues and mouths, and does not involve volunteers'\n facial information. The dataset only contains photos and feature labels,\n and does not contain any personal privacy information of volunteers. ###\n Files and variables The dataset consists of three folders, namely\n shezhenv3 coco, shezhenv3 txt, and shezhenv3 xml, which can be used by\n three different object detection models. The image data in the three\n folders is the same, but the label files are not the same. Users can\n choose the file that suits them according to their own needs. These three\n folders all contain test sets, training sets, and validation sets, each\n containing image data and corresponding label data. shezhenv3-coco: A\n collection of data with tag format in coco format Shezhenv3-txt: A\n collection of data with tag format in txt format Shezhenv3 xml: A\n collection of data with tag format in XML format test: test set train:\n training set val: verification set images: folder for storing image data\n labels: folder for storing label data annotations: JSON-formatted data\n classes.txt: Introduction to the names of various labels #### File:\n shezhen_datasets1.zip **Description:** Among them, there are 5594 images\n in the training set, 572 images in the validation set, and 553 images in\n the test set. Contains three annotation formats: coco/. txt/. xml, which\n can be used for experiments using relevant object detection algorithms\n through configuration files. The label data is divided into twenty\n categories, arranged and classified from 0 to 19, as follows:\n 0:jiankangshe; 1:baotaishe; 2:hongshe; 3:zishe; 4:pangdashe; 5:shoushe;\n 6:hongdianshe; 7:liewenshe; 8:chihenshe; 9:baitaishe; 10:huangtaishe;\n 11:heitaishe; 12:huataishe; 13:shenquao; 14:shenqutu; 15:gandanao;\n 16:gandantu; 17:piweiao; 18:xinfeitu; 19:xinfeiao; ## Code/software After\n downloading, use a file decompressor to decompress the file, and then use\n an image viewer to view the data. You can use any universal file\n decompressor and image viewer to perform the above operations. When using\n this dataset, it is recommended to use YOLO series models such as\n YOLOV8/V11/V12. The source code of these models can be downloaded directly\n from GitHub as open source code. Before use, please create a new YAML\n format file with 0: jiankangse; 1:baotaishe; 2:hongshe; 3:zishe;\n 4:pangdashe; 5:shoushe; 6:hongdianshe; 7:liewenshe; 8:chihenshe;\n 9:baitaishe; 10:huangtaishe; 11:heitaishe; 12:huataishe; 13:shenquao;\n 14:shenqutu; 15:gandanao; 16:gandantu; 17:piweiao; 18:xinfeitu;\n 19:xinfeiao; Write it in and set the path of the dataset according to your\n computer configuration. ## Access information Other publicly accessible\n locations of the data:\n [https://github.com/m28805746-max/Intelligent-tongue-diagnosis-detection-dataset](https://github.com/m28805746-max/Intelligent-tongue-diagnosis-detection-dataset) Data was derived from the following sources: * This dataset is our original data, collected by the device and labeled according to the doctor's advice","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[],"xml":"<?xml version="1.0" encoding="UTF-8"?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4/metadata.xsd">
  <identifier identifierType="DOI">10.5061/DRYAD.1C59ZW48R</identifier>
  <creators>
    <creator>
      <creatorName nameType="Personal">Longfei, Gao</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0009-0007-1261-0227</nameIdentifier>
      <affiliation affiliationIdentifier="https://ror.org/013e0zm98" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Beijing Technology and Business University</affiliation>
    </creator>
  </creators>
  <titles>
    <title>
      TMC-Tongue: A standardized tongue image dataset with pathological
      annotations for AI-assisted TCM diagnosis
    </title>
  </titles>
  <publisher publisherIdentifier="https://ror.org/00x6h5n95" publisherIdentifierScheme="ROR" schemeURI="https://ror.org/">Dryad</publisher>
  <resourceType resourceTypeGeneral="Dataset">dataset</resourceType>
  <publicationYear>2026</publicationYear>
  <subjects>
    <subject subjectScheme="fos" schemeURI="https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf">FOS: Basic medicine</subject>
    <subject subjectScheme="fos" schemeURI="https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf">FOS: Computer and information sciences</subject>
    <subject subjectScheme="fos" schemeURI="https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf">FOS: Medical and health sciences</subject>
    <subject subjectScheme="PLOS Subject Area Thesaurus" schemeURI="https://github.com/PLOS/plos-thesaurus">Target detection</subject>
    <subject subjectScheme="PLOS Subject Area Thesaurus" schemeURI="https://github.com/PLOS/plos-thesaurus">Traditional Chinese medicine</subject>
    <subject subjectScheme="PLOS Subject Area Thesaurus" schemeURI="https://github.com/PLOS/plos-thesaurus">Tongue</subject>
  </subjects>
  <dates>
    <date dateType="Created">2025-09-23T03:25:17Z</date>
    <date dateType="Submitted">2025-09-23T03:29:40Z</date>
    <date dateType="Issued">2026-01-06T00:00:00Z</date>
    <date dateType="Available">2026-01-06T00:00:00Z</date>
  </dates>
  <language>en</language>
  <relatedIdentifiers>
    <relatedIdentifier relationType="IsSupplementedBy" relatedIdentifierType="URL">https://github.com/m28805746-max/Intelligent-tongue-diagnosis-detection-dataset</relatedIdentifier>
    <relatedIdentifier relationType="IsCitedBy" relatedIdentifierType="DOI">https://doi.org/10.62762/bish.2026.303296</relatedIdentifier>
  </relatedIdentifiers>
  <sizes>
    <size>2343410168 bytes</size>
  </sizes>
  <version>5</version>
  <rightsList>
    <rights rightsURI="https://spdx.org/licenses/CC0-1.0.html">Creative Commons Zero v1.0 Universal</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">
      This dataset contains 21 disease categories that can be used for target
      detection in tongue diagnosis. The categories are jiankangshe (Healthy
      Tongue), botaishe (Tongue with peeling coating), hongshe (Red tongue),
      zishe (Purple tongue), pangdashe (Chubby tongue), shoushe (Thin tongue),
      hongdianshe (Red dot tongue), liewenshe (Cracked tongue), chihenshe
      (Dentate tongue), baitaishe (White coating tongue), huangtaishe (Yellow
      coating tongue), heitaishe (Black coating tongue), huataishe (Smooth
      coating tongue), shenquao (renal depression), shenqutu (renal protrusion),
      gandanao (Hepatobiliary depression), gandantu (Hepatobiliary protrusion),
      piweiao (spleen and stomach depression), xinfeitu (heart and lung
      protrusion), xinfeiao (heart and lung depression), corresponding numerical
      order is 0-19. Among them, there are 5594 images in the training set, 572
      images in the validation set, and 553 images in the test set. Contains
      three annotation formats: coco/. txt/. XML, which can be used for
      experiments using relevant object detection algorithms through
      configuration files.
    </description>
    <description descriptionType="TechnicalInfo">
      # TMC-Tongue: A standardized tongue image dataset with pathological
      annotations for AI-assisted TCM diagnosis Dataset DOI:
      [10.5061/dryad.1c59zw48r](10.5061/dryad.1c59zw48r) ## Description of the
      data and file structure Using high-precision cameras to capture
      volunteers' tongues to obtain data, all data only includes
      volunteers' tongues and mouths, and does not involve volunteers'
      facial information. The dataset only contains photos and feature labels,
      and does not contain any personal privacy information of volunteers. ###
      Files and variables The dataset consists of three folders, namely
      shezhenv3 coco, shezhenv3 txt, and shezhenv3 xml, which can be used by
      three different object detection models. The image data in the three
      folders is the same, but the label files are not the same. Users can
      choose the file that suits them according to their own needs. These three
      folders all contain test sets, training sets, and validation sets, each
      containing image data and corresponding label data. shezhenv3-coco: A
      collection of data with tag format in coco format Shezhenv3-txt: A
      collection of data with tag format in txt format Shezhenv3 xml: A
      collection of data with tag format in XML format test: test set train:
      training set val: verification set images: folder for storing image data
      labels: folder for storing label data annotations: JSON-formatted data
      classes.txt: Introduction to the names of various labels #### File:
      shezhen_datasets1.zip **Description:** Among them, there are 5594 images
      in the training set, 572 images in the validation set, and 553 images in
      the test set. Contains three annotation formats: coco/. txt/. xml, which
      can be used for experiments using relevant object detection algorithms
      through configuration files. The label data is divided into twenty
      categories, arranged and classified from 0 to 19, as follows:
      0:jiankangshe; 1:baotaishe; 2:hongshe; 3:zishe; 4:pangdashe; 5:shoushe;
      6:hongdianshe; 7:liewenshe; 8:chihenshe; 9:baitaishe; 10:huangtaishe;
      11:heitaishe; 12:huataishe; 13:shenquao; 14:shenqutu; 15:gandanao;
      16:gandantu; 17:piweiao; 18:xinfeitu; 19:xinfeiao; ## Code/software After
      downloading, use a file decompressor to decompress the file, and then use
      an image viewer to view the data. You can use any universal file
      decompressor and image viewer to perform the above operations. When using
      this dataset, it is recommended to use YOLO series models such as
      YOLOV8/V11/V12. The source code of these models can be downloaded directly
      from GitHub as open source code. Before use, please create a new YAML
      format file with 0: jiankangse; 1:baotaishe; 2:hongshe; 3:zishe;
      4:pangdashe; 5:shoushe; 6:hongdianshe; 7:liewenshe; 8:chihenshe;
      9:baitaishe; 10:huangtaishe; 11:heitaishe; 12:huataishe; 13:shenquao;
      14:shenqutu; 15:gandanao; 16:gandantu; 17:piweiao; 18:xinfeitu;
      19:xinfeiao; Write it in and set the path of the dataset according to your
      computer configuration. ## Access information Other publicly accessible
      locations of the data:
      [https://github.com/m28805746-max/Intelligent-tongue-diagnosis-detection-dataset](https://github.com/m28805746-max/Intelligent-tongue-diagnosis-detection-dataset) Data was derived from the following sources: * This dataset is our original data, collected by the device and labeled according to the doctor's advice
    </description>
  </descriptions>
</resource>","url":"https://datadryad.org/dataset/doi:10.5061/dryad.1c59zw48r","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":3179,"viewsOverTime":[{"yearMonth":"2025-10","total":0},{"yearMonth":"2026-01","total":137},{"yearMonth":"2026-02","total":120},{"yearMonth":"2026-03","total":199},{"yearMonth":"2026-04","total":331},{"yearMonth":"2026-05","total":432},{"yearMonth":"2026-06","total":470},{"yearMonth":"2026-07","total":420},{"yearMonth":"2026-08","total":554},{"yearMonth":"2026-09","total":516}],"downloadCount":1267,"downloadsOverTime":[{"yearMonth":"2025-10","total":3},{"yearMonth":"2026-01","total":57},{"yearMonth":"2026-02","total":57},{"yearMonth":"2026-03","total":82},{"yearMonth":"2026-04","total":147},{"yearMonth":"2026-05","total":192},{"yearMonth":"2026-06","total":201},{"yearMonth":"2026-07","total":169},{"yearMonth":"2026-08","total":174},{"yearMonth":"2026-09","total":185}],"referenceCount":0,"citationCount":1,"citationsOverTime":[{"year":"2026","total":1}],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-01-06T20:07:37.000Z","registered":"2026-01-06T20:07:38.000Z","published":"2026","updated":"2026-03-16T19:30:50.000Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}},"provider":{"data":{"id":"dryad","type":"providers"}},"media":{"data":{"id":"10.5061/dryad.1c59zw48r","type":"media"}},"references":{"data":[]},"citations":{"data":[{"id":"10.62762/bish.2026.303296","type":"dois"}]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[]}}}}