DOI: 10.5061/dryad.1c59zw48r
title: TMC-Tongue: A standardized tongue image dataset with pathological annotations for AI-assisted TCM diagnosis

description: 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: # 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