A detection model training method, comprising: determining, by a training apparatus, an initial training model, the initial training model comprising an initial detection model and an adaptive model; determining, by the training apparatus, a training sample, the training sample comprising source domain data and target domain data, the source domain data comprising a plurality of first colorectal images, each first colorectal image comprising: a first identifier of whether a polyp is comprised, and a second identifier of a domain that the each first colorectal image belongs to; the target domain data comprising: a plurality of second colorectal images, and a third identifier of a domain that each second colorectal image belongs to, wherein part of the first colorectal images comprise polyps, and the source domain data further comprises types and locations of the polyps comprised in the part of the first colorectal images; separately determining, by the training apparatus, whether a polyp is comprised in the each first colorectal image through the initial detection model according to a feature of the each first colorectal image, and determining a detection result; separately determining, by the training apparatus, the types and the locations of the polyps comprised in the part of the first colorectal images through the initial detection model, and determining a target detection result; separately determining, by the training apparatus, a domain that each colorectal image in the training sample belongs to through the adaptive model according to a feature of the each colorectal image, and determining a domain classification result; calculating, by the training apparatus, a loss function value related to the initial training model according to the detection result, the domain classification result, the target detection result, the first identifier, the second identifier, and the third identifier, wherein the loss function value related to the initial training model comprises: a function-calculated value of a detection loss function and an adaptive loss function; the detection loss function comprises: a third error between the types and the locations, that are of the polyps in the part of the first colorectal images and that are determined according to the initial detection model, and the types and the locations of the polyps in the part of the first colorectal images labeled in the source domain data; adjusting, by the training apparatus, a fixed parameter value in the initial training model according to the loss function value, and determining a final detection model; and determining, by the training apparatus, whether a polyp is included in the second colorectal image according to the final detection model, and determining, if a polyp is included in the second colorectal image, a type and a location of the polyp. determining, by a training apparatus, an initial training model, the initial training model comprising an initial detection model and an adaptive model; determining, by the training apparatus, a training sample, the training sample comprising source domain data and target domain data, the source domain data comprising a plurality of first colorectal images, each first colorectal image comprising: a first identifier of whether a polyp is comprised, and a second identifier of a domain that the each first colorectal image belongs to; the target domain data comprising: a plurality of second colorectal images, and a third identifier of a domain that each second colorectal image belongs to, wherein part of the first colorectal images comprise polyps, and the source domain data further comprises types and locations of the polyps comprised in the part of the first colorectal images; separately determining, by the training apparatus, whether a polyp is comprised in the each first colorectal image through the initial detection model according to a feature of the each first colorectal image, and determining a detection result; separately determining, by the training apparatus, the types and the locations of the polyps comprised in the part of the first colorectal images through the initial detection model, and determining a target detection result; separately determining, by the training apparatus, a domain that each colorectal image in the training sample belongs to through the adaptive model according to a feature of the each colorectal image, and determining a domain classification result; calculating, by the training apparatus, a loss function value related to the initial training model according to the detection result, the domain classification result, the target detection result, the first identifier, the second identifier, and the third identifier, wherein the loss function value related to the initial training model comprises: a function-calculated value of a detection loss function and an adaptive loss function; the detection loss function comprises: a third error between the types and the locations, that are of the polyps in the part of the first colorectal images and that are determined according to the initial detection model, and the types and the locations of the polyps in the part of the first colorectal images labeled in the source domain data; adjusting, by the training apparatus, a fixed parameter value in the initial training model according to the loss function value, and determining a final detection model; and determining, by the training apparatus, whether a polyp is included in the second colorectal image according to the final detection model, and determining, if a polyp is included in the second colorectal image, a type and a location of the polyp.