feat(auto-annotation): integrate YOLO auto-labeling and enhance data management (#223)

* feat(auto-annotation): initial setup

* chore: remove package-lock.json

* chore: 清理本地测试脚本与 Maven 设置

* chore: change package-lock.json

backend/openapi/specs/data-management.yaml

@@ -231,6 +231,29 @@ paths:
               schema:
                 $ref: '#/components/schemas/PagedDatasetFileResponse'
 
+  /data-management/datasets/{datasetId}/files/directories:
+    post:
+      tags: [ DatasetFile ]
+      operationId: createDirectory
+      summary: 在数据集下创建子目录
+      description: 在指定数据集下的某个前缀路径中创建一个新的子目录
+      parameters:
+        - name: datasetId
+          in: path
+          required: true
+          schema:
+            type: string
+          description: 数据集ID
+      requestBody:
+        required: true
+        content:
+          application/json:
+            schema:
+              $ref: '#/components/schemas/CreateDirectoryRequest'
+      responses:
+        '200':
+          description: 创建成功
+
   /data-management/datasets/{datasetId}/files/{fileId}:
     get:
       tags: [DatasetFile]
@@ -635,8 +658,23 @@ components:
           type: integer
           format: int64
           description: 总文件大小（字节）
+        prefix:
+          type: string
+          description: 目标子目录前缀，例如 "images/"，为空表示数据集根目录
       required: [ totalFileNum ]
 
+    CreateDirectoryRequest:
+      type: object
+      description: 创建数据集子目录请求
+      properties:
+        parentPrefix:
+          type: string
+          description: 父级前缀路径，例如 "images/"，为空表示数据集根目录
+        directoryName:
+          type: string
+          description: 新建目录名称
+      required: [ directoryName ]
+
     UploadFileRequest:
       type: object
       description: 分片上传请求

backend/services/data-management-service/src/main/java/com/datamate/datamanagement/application/DatasetFileApplicationService.java

@@ -24,6 +24,7 @@ import com.datamate.datamanagement.infrastructure.persistence.repository.Dataset
 import com.datamate.datamanagement.interfaces.converter.DatasetConverter;
 import com.datamate.datamanagement.interfaces.dto.AddFilesRequest;
 import com.datamate.datamanagement.interfaces.dto.CopyFilesRequest;
+import com.datamate.datamanagement.interfaces.dto.CreateDirectoryRequest;
 import com.datamate.datamanagement.interfaces.dto.UploadFileRequest;
 import com.datamate.datamanagement.interfaces.dto.UploadFilesPreRequest;
 import com.fasterxml.jackson.core.JsonProcessingException;
@@ -149,13 +150,48 @@ public class DatasetFileApplicationService {
         }
         datasetFile.setFileName(path.getFileName().toString());
         datasetFile.setUploadTime(localDateTime);
+
+        // 目录与普通文件区分处理
         if (Files.isDirectory(path)) {
             datasetFile.setId("directory-" + datasetFile.getFileName());
-        } else if (Objects.isNull(datasetFilesMap.get(path.toString()))) {
+            datasetFile.setDirectory(true);
+
+            // 统计目录下文件数量和总大小
+            try {
+                long fileCount;
+                long totalSize;
+
+                try (Stream<Path> walk = Files.walk(path)) {
+                    fileCount = walk.filter(Files::isRegularFile).count();
+                }
+
+                try (Stream<Path> walk = Files.walk(path)) {
+                    totalSize = walk
+                        .filter(Files::isRegularFile)
+                        .mapToLong(p -> {
+                            try {
+                                return Files.size(p);
+                            } catch (IOException e) {
+                                log.error("get file size error", e);
+                                return 0L;
+                            }
+                        })
+                        .sum();
+                }
+
+                datasetFile.setFileCount(fileCount);
+                datasetFile.setFileSize(totalSize);
+            } catch (IOException e) {
+                log.error("stat directory info error", e);
+            }
+        } else {
+            DatasetFile exist = datasetFilesMap.get(path.toString());
+            if (exist == null) {
                 datasetFile.setId("file-" + datasetFile.getFileName());
                 datasetFile.setFileSize(path.toFile().length());
             } else {
-            datasetFile = datasetFilesMap.get(path.toString());
+                datasetFile = exist;
+            }
         }
         return datasetFile;
     }
@@ -291,13 +327,27 @@ public class DatasetFileApplicationService {
         if (Objects.isNull(datasetRepository.getById(datasetId))) {
             throw BusinessException.of(DataManagementErrorCode.DATASET_NOT_FOUND);
         }
+        
+        // 构建上传路径，如果有 prefix 则追加到路径中
+        String prefix = Optional.ofNullable(chunkUploadRequest.getPrefix()).orElse("").trim();
+        prefix = prefix.replace("\\", "/");
+        while (prefix.startsWith("/")) {
+            prefix = prefix.substring(1);
+        }
+        
+        String uploadPath = datasetBasePath + File.separator + datasetId;
+        if (!prefix.isEmpty()) {
+            uploadPath = uploadPath + File.separator + prefix.replace("/", File.separator);
+        }
+        
         ChunkUploadPreRequest request = ChunkUploadPreRequest.builder().build();
-        request.setUploadPath(datasetBasePath + File.separator + datasetId);
+        request.setUploadPath(uploadPath);
         request.setTotalFileNum(chunkUploadRequest.getTotalFileNum());
         request.setServiceId(DatasetConstant.SERVICE_ID);
         DatasetFileUploadCheckInfo checkInfo = new DatasetFileUploadCheckInfo();
         checkInfo.setDatasetId(datasetId);
         checkInfo.setHasArchive(chunkUploadRequest.isHasArchive());
+        checkInfo.setPrefix(prefix);
         try {
             ObjectMapper objectMapper = new ObjectMapper();
             String checkInfoJson = objectMapper.writeValueAsString(checkInfo);
@@ -368,6 +418,211 @@ public class DatasetFileApplicationService {
         datasetRepository.updateById(dataset);
     }
 
+    /**
+     * 在数据集下创建子目录
+     */
+    @Transactional
+    public void createDirectory(String datasetId, CreateDirectoryRequest req) {
+        Dataset dataset = datasetRepository.getById(datasetId);
+        if (dataset == null) {
+            throw BusinessException.of(DataManagementErrorCode.DATASET_NOT_FOUND);
+        }
+        String datasetPath = dataset.getPath();
+        String parentPrefix = Optional.ofNullable(req.getParentPrefix()).orElse("").trim();
+        parentPrefix = parentPrefix.replace("\\", "/");
+        while (parentPrefix.startsWith("/")) {
+            parentPrefix = parentPrefix.substring(1);
+        }
+
+        String directoryName = Optional.ofNullable(req.getDirectoryName()).orElse("").trim();
+        if (directoryName.isEmpty()) {
+            throw BusinessException.of(CommonErrorCode.PARAM_ERROR);
+        }
+        if (directoryName.contains("..") || directoryName.contains("/") || directoryName.contains("\\")) {
+            throw BusinessException.of(CommonErrorCode.PARAM_ERROR);
+        }
+
+        Path basePath = Paths.get(datasetPath);
+        Path targetPath = parentPrefix.isEmpty()
+            ? basePath.resolve(directoryName)
+            : basePath.resolve(parentPrefix).resolve(directoryName);
+
+        Path normalized = targetPath.normalize();
+        if (!normalized.startsWith(basePath)) {
+            throw BusinessException.of(CommonErrorCode.PARAM_ERROR);
+        }
+
+        try {
+            Files.createDirectories(normalized);
+        } catch (IOException e) {
+            log.error("Failed to create directory {} for dataset {}", normalized, datasetId, e);
+            throw BusinessException.of(SystemErrorCode.FILE_SYSTEM_ERROR);
+        }
+    }
+
+    /**
+     * 下载目录为 ZIP 文件
+     */
+    @Transactional(readOnly = true)
+    public void downloadDirectory(String datasetId, String prefix, HttpServletResponse response) {
+        Dataset dataset = datasetRepository.getById(datasetId);
+        if (dataset == null) {
+            throw BusinessException.of(DataManagementErrorCode.DATASET_NOT_FOUND);
+        }
+
+        String datasetPath = dataset.getPath();
+        prefix = Optional.ofNullable(prefix).orElse("").trim();
+        prefix = prefix.replace("\\", "/");
+        while (prefix.startsWith("/")) {
+            prefix = prefix.substring(1);
+        }
+        while (prefix.endsWith("/")) {
+            prefix = prefix.substring(0, prefix.length() - 1);
+        }
+
+        Path basePath = Paths.get(datasetPath);
+        Path targetPath = prefix.isEmpty() ? basePath : basePath.resolve(prefix);
+        Path normalized = targetPath.normalize();
+
+        if (!normalized.startsWith(basePath)) {
+            throw BusinessException.of(CommonErrorCode.PARAM_ERROR);
+        }
+
+        if (!Files.exists(normalized) || !Files.isDirectory(normalized)) {
+            throw BusinessException.of(DataManagementErrorCode.DIRECTORY_NOT_FOUND);
+        }
+
+        String zipFileName = prefix.isEmpty() ? dataset.getName() : prefix.replace("/", "_");
+        zipFileName = zipFileName + "_" + LocalDateTime.now().format(DateTimeFormatter.ofPattern("yyyyMMdd_HHmmss")) + ".zip";
+
+        try {
+            response.setContentType("application/zip");
+            response.setHeader(HttpHeaders.CONTENT_DISPOSITION, "attachment; filename=\"" + zipFileName + "\"");
+            
+            try (ZipArchiveOutputStream zipOut = new ZipArchiveOutputStream(response.getOutputStream())) {
+                zipDirectory(normalized, normalized, zipOut);
+                zipOut.finish();
+            }
+        } catch (IOException e) {
+            log.error("Failed to download directory {} for dataset {}", normalized, datasetId, e);
+            throw BusinessException.of(SystemErrorCode.FILE_SYSTEM_ERROR);
+        }
+    }
+
+    /**
+     * 递归压缩目录
+     */
+    private void zipDirectory(Path sourceDir, Path basePath, ZipArchiveOutputStream zipOut) throws IOException {
+        try (Stream<Path> paths = Files.walk(sourceDir)) {
+            paths.filter(path -> !Files.isDirectory(path))
+                .forEach(path -> {
+                    try {
+                        Path relativePath = basePath.relativize(path);
+                        ZipArchiveEntry zipEntry = new ZipArchiveEntry(relativePath.toString());
+                        zipOut.putArchiveEntry(zipEntry);
+                        try (InputStream fis = Files.newInputStream(path)) {
+                            IOUtils.copy(fis, zipOut);
+                        }
+                        zipOut.closeArchiveEntry();
+                    } catch (IOException e) {
+                        log.error("Failed to add file to zip: {}", path, e);
+                    }
+                });
+        }
+    }
+
+    /**
+     * 删除目录及其所有内容
+     */
+    @Transactional
+    public void deleteDirectory(String datasetId, String prefix) {
+        Dataset dataset = datasetRepository.getById(datasetId);
+        if (dataset == null) {
+            throw BusinessException.of(DataManagementErrorCode.DATASET_NOT_FOUND);
+        }
+
+        prefix = Optional.ofNullable(prefix).orElse("").trim();
+        prefix = prefix.replace("\\", "/");
+        while (prefix.startsWith("/")) {
+            prefix = prefix.substring(1);
+        }
+        while (prefix.endsWith("/")) {
+            prefix = prefix.substring(0, prefix.length() - 1);
+        }
+
+        if (prefix.isEmpty()) {
+            throw BusinessException.of(CommonErrorCode.PARAM_ERROR);
+        }
+
+        String datasetPath = dataset.getPath();
+        Path basePath = Paths.get(datasetPath);
+        Path targetPath = basePath.resolve(prefix);
+        Path normalized = targetPath.normalize();
+
+        if (!normalized.startsWith(basePath)) {
+            throw BusinessException.of(CommonErrorCode.PARAM_ERROR);
+        }
+
+        if (!Files.exists(normalized) || !Files.isDirectory(normalized)) {
+            throw BusinessException.of(DataManagementErrorCode.DIRECTORY_NOT_FOUND);
+        }
+
+        // 删除数据库中该目录下的所有文件记录（基于数据集内相对路径判断）
+        String datasetPathNorm = datasetPath.replace("\\", "/");
+        String logicalPrefix = prefix; // 已经去掉首尾斜杠
+        List<DatasetFile> filesToDelete = datasetFileRepository.findAllByDatasetId(datasetId).stream()
+            .filter(file -> {
+                if (file.getFilePath() == null) {
+                    return false;
+                }
+                String filePath = file.getFilePath().replace("\\", "/");
+                if (!filePath.startsWith(datasetPathNorm)) {
+                    return false;
+                }
+                String relative = filePath.substring(datasetPathNorm.length());
+                while (relative.startsWith("/")) {
+                    relative = relative.substring(1);
+                }
+                return relative.equals(logicalPrefix) || relative.startsWith(logicalPrefix + "/");
+            })
+            .collect(Collectors.toList());
+
+        for (DatasetFile file : filesToDelete) {
+            datasetFileRepository.removeById(file.getId());
+        }
+
+        // 删除文件系统中的目录
+        try {
+            deleteDirectoryRecursively(normalized);
+        } catch (IOException e) {
+            log.error("Failed to delete directory {} for dataset {}", normalized, datasetId, e);
+            throw BusinessException.of(SystemErrorCode.FILE_SYSTEM_ERROR);
+        }
+
+        // 更新数据集
+        dataset.setFiles(filesToDelete);
+        for (DatasetFile file : filesToDelete) {
+            dataset.removeFile(file);
+        }
+        datasetRepository.updateById(dataset);
+    }
+
+    /**
+     * 递归删除目录
+     */
+    private void deleteDirectoryRecursively(Path directory) throws IOException {
+        try (Stream<Path> paths = Files.walk(directory)) {
+            paths.sorted(Comparator.reverseOrder())
+                .forEach(path -> {
+                    try {
+                        Files.delete(path);
+                    } catch (IOException e) {
+                        log.error("Failed to delete: {}", path, e);
+                    }
+                });
+        }
+    }
+
     /**
      * 为数据集文件设置文件id
      *

backend/services/data-management-service/src/main/java/com/datamate/datamanagement/domain/model/dataset/DatasetFile.java

@@ -1,5 +1,6 @@
 package com.datamate.datamanagement.domain.model.dataset;
 
+import com.baomidou.mybatisplus.annotation.TableField;
 import com.baomidou.mybatisplus.annotation.TableId;
 import com.baomidou.mybatisplus.annotation.TableName;
 import com.fasterxml.jackson.core.type.TypeReference;
@@ -38,6 +39,14 @@ public class DatasetFile {
     private LocalDateTime createdAt;
     private LocalDateTime updatedAt;
 
+    /** 标记是否为目录（非持久化字段） */
+    @TableField(exist = false)
+    private Boolean directory;
+
+    /** 目录包含的文件数量（非持久化字段） */
+    @TableField(exist = false)
+    private Long fileCount;
+
     /**
      * 解析标签
      *

backend/services/data-management-service/src/main/java/com/datamate/datamanagement/domain/model/dataset/DatasetFileUploadCheckInfo.java

@@ -18,4 +18,7 @@ public class DatasetFileUploadCheckInfo {
 
     /** 是否为压缩包上传 */
     private boolean hasArchive;
+
+    /** 目标子目录前缀，例如 "images/"，为空表示数据集根目录 */
+    private String prefix;
 }

backend/services/data-management-service/src/main/java/com/datamate/datamanagement/infrastructure/exception/DataManagementErrorCode.java

@@ -34,9 +34,13 @@ public enum DataManagementErrorCode implements ErrorCode {
      */
     DATASET_TAG_ALREADY_EXISTS("data_management.0005", "数据集标签已存在"),
     /**
-     * 数据集标签已存在
+     * 数据集文件已存在
      */
-    DATASET_FILE_ALREADY_EXISTS("data_management.0006", "数据集文件已存在");
+    DATASET_FILE_ALREADY_EXISTS("data_management.0006", "数据集文件已存在"),
+    /**
+     * 目录不存在
+     */
+    DIRECTORY_NOT_FOUND("data_management.0007", "目录不存在");
 
     private final String code;
     private final String message;

backend/services/data-management-service/src/main/java/com/datamate/datamanagement/interfaces/dto/CreateDirectoryRequest.java

@@ -0,0 +1,20 @@
+package com.datamate.datamanagement.interfaces.dto;
+
+import jakarta.validation.constraints.NotBlank;
+import lombok.Getter;
+import lombok.Setter;
+
+/**
+ * 创建数据集子目录请求
+ */
+@Getter
+@Setter
+public class CreateDirectoryRequest {
+
+    /** 父级前缀路径，例如 "images/"，为空表示数据集根目录 */
+    private String parentPrefix;
+
+    /** 新建目录名称 */
+    @NotBlank
+    private String directoryName;
+}

backend/services/data-management-service/src/main/java/com/datamate/datamanagement/interfaces/dto/DatasetFileResponse.java

@@ -33,4 +33,8 @@ public class DatasetFileResponse {
     private LocalDateTime lastAccessTime;
     /** 上传者 */
     private String uploadedBy;
+    /** 是否为目录 */
+    private Boolean directory;
+    /** 目录文件数量 */
+    private Long fileCount;
 }

backend/services/data-management-service/src/main/java/com/datamate/datamanagement/interfaces/dto/UploadFilesPreRequest.java

@@ -19,4 +19,7 @@ public class UploadFilesPreRequest {
 
     /** 总文件大小 */
     private long totalSize;
+
+    /** 目标子目录前缀，例如 "images/"，为空表示数据集根目录 */
+    private String prefix;
 }

backend/services/data-management-service/src/main/java/com/datamate/datamanagement/interfaces/rest/DatasetFileController.java

@@ -10,6 +10,7 @@ import com.datamate.datamanagement.domain.model.dataset.DatasetFile;
 import com.datamate.datamanagement.interfaces.converter.DatasetConverter;
 import com.datamate.datamanagement.interfaces.dto.AddFilesRequest;
 import com.datamate.datamanagement.interfaces.dto.CopyFilesRequest;
+import com.datamate.datamanagement.interfaces.dto.CreateDirectoryRequest;
 import com.datamate.datamanagement.interfaces.dto.DatasetFileResponse;
 import com.datamate.datamanagement.interfaces.dto.UploadFileRequest;
 import com.datamate.datamanagement.interfaces.dto.UploadFilesPreRequest;
@@ -162,4 +163,35 @@ public class DatasetFileController {
         List<DatasetFile> datasetFiles = datasetFileApplicationService.addFilesToDataset(datasetId, req);
         return DatasetConverter.INSTANCE.convertToResponseList(datasetFiles);
     }
+
+    /**
+     * 在数据集下创建子目录
+     */
+    @PostMapping("/directories")
+    public ResponseEntity<Void> createDirectory(@PathVariable("datasetId") String datasetId,
+                                                @RequestBody @Valid CreateDirectoryRequest req) {
+        datasetFileApplicationService.createDirectory(datasetId, req);
+        return ResponseEntity.ok().build();
+    }
+
+    /**
+     * 下载目录（压缩为 ZIP）
+     */
+    @IgnoreResponseWrap
+    @GetMapping(value = "/directories/download", produces = "application/zip")
+    public void downloadDirectory(@PathVariable("datasetId") String datasetId,
+                                   @RequestParam(value = "prefix", required = false, defaultValue = "") String prefix,
+                                   HttpServletResponse response) {
+        datasetFileApplicationService.downloadDirectory(datasetId, prefix, response);
+    }
+
+    /**
+     * 删除目录及其所有内容
+     */
+    @DeleteMapping("/directories")
+    public ResponseEntity<Void> deleteDirectory(@PathVariable("datasetId") String datasetId,
+                                                @RequestParam(value = "prefix", required = false, defaultValue = "") String prefix) {
+        datasetFileApplicationService.deleteDirectory(datasetId, prefix);
+        return ResponseEntity.ok().build();
+    }
 }

backend/shared/domain-common/src/main/java/com/datamate/common/infrastructure/exception/CommonErrorCode.java

@@ -11,6 +11,7 @@ import lombok.Getter;
 @Getter
 @AllArgsConstructor
 public enum CommonErrorCode implements ErrorCode{
+    PARAM_ERROR("common.0001", "参数错误"),
     PRE_UPLOAD_REQUEST_NOT_EXIST("common.0101", "预上传请求不存在");
     private final String code;
     private final String message;

frontend/src/components/CardView.tsx

@@ -199,15 +199,11 @@ function CardView<T extends BaseCardDataType>(props: CardViewProps<T>) {
                             ? ""
                             : "bg-gradient-to-br from-sky-300 to-blue-500 text-white"
                         }`}
-                        style={{
-                          ...(item?.iconColor
+                        style={
+                          item?.iconColor
                             ? { backgroundColor: item.iconColor }
-                            : {}),
-                          backgroundImage:
-                            "linear-gradient(180deg, rgba(255,255,255,0.35), rgba(255,255,255,0.05))",
-                          boxShadow:
-                            "inset 0 0 0 1px rgba(255,255,255,0.25)",
-                        }}
+                            : {}
+                        }
                       >
                         <div className="w-[2.1rem] h-[2.1rem] text-gray-50">{item?.icon}</div>
                       </div>

frontend/src/components/business/DatasetFileTransfer.tsx

@@ -1,5 +1,5 @@
 import React, { useCallback, useEffect } from "react";
-import { Button, Input, Table } from "antd";
+import { Button, Input, Table, message } from "antd";
 import { RightOutlined } from "@ant-design/icons";
 import { mapDataset } from "@/pages/DataManagement/dataset.const";
 import {
@@ -20,6 +20,7 @@ interface DatasetFileTransferProps
   selectedFilesMap: { [key: string]: DatasetFile };
   onSelectedFilesChange: (filesMap: { [key: string]: DatasetFile }) => void;
   onDatasetSelect?: (dataset: Dataset | null) => void;
+  datasetTypeFilter?: DatasetType;
 }
 
 const fileCols = [
@@ -50,6 +51,7 @@ const DatasetFileTransfer: React.FC<DatasetFileTransferProps> = ({
   selectedFilesMap,
   onSelectedFilesChange,
   onDatasetSelect,
+  datasetTypeFilter = DatasetType.TEXT,
   ...props
 }) => {
   const [datasets, setDatasets] = React.useState<Dataset[]>([]);
@@ -75,6 +77,7 @@ const DatasetFileTransfer: React.FC<DatasetFileTransferProps> = ({
   const [datasetSelections, setDatasetSelections] = React.useState<Dataset[]>(
     []
   );
+  const [selectingAll, setSelectingAll] = React.useState<boolean>(false);
 
   const fetchDatasets = async () => {
     const { data } = await queryDatasetsUsingGet({
@@ -82,7 +85,7 @@ const DatasetFileTransfer: React.FC<DatasetFileTransferProps> = ({
       page: datasetPagination.current,
       size: datasetPagination.pageSize,
       keyword: datasetSearch,
-      type: DatasetType.TEXT,
+      type: datasetTypeFilter,
     });
     setDatasets(data.content.map(mapDataset) || []);
     setDatasetPagination((prev) => ({
@@ -116,7 +119,8 @@ const DatasetFileTransfer: React.FC<DatasetFileTransferProps> = ({
       setFiles(
         (data.content || []).map((item: DatasetFile) => ({
           ...item,
-          key: item.id,
+          id: item.id,
+          key: String(item.id), // rowKey 使用字符串，确保与 selectedRowKeys 类型一致
           datasetName: selectedDataset.name,
         }))
       );
@@ -134,7 +138,8 @@ const DatasetFileTransfer: React.FC<DatasetFileTransferProps> = ({
     // 当数据集变化时，重置文件分页并拉取第一页文件，避免额外的循环请求
     if (selectedDataset) {
       setFilesPagination({ current: 1, pageSize: 10, total: 0 });
-      fetchFiles({ page: 1, pageSize: 10 }).catch(() => {});
+      // 后端 page 参数为 0-based，这里传 0 获取第一页
+      fetchFiles({ page: 0, pageSize: 10 }).catch(() => {});
     } else {
       setFiles([]);
       setFilesPagination({ current: 1, pageSize: 10, total: 0 });
@@ -147,6 +152,73 @@ const DatasetFileTransfer: React.FC<DatasetFileTransferProps> = ({
     onDatasetSelect?.(selectedDataset);
   }, [selectedDataset, onDatasetSelect]);
 
+  const handleSelectAllInDataset = useCallback(async () => {
+    if (!selectedDataset) {
+      message.warning("请先选择一个数据集");
+      return;
+    }
+
+    try {
+      setSelectingAll(true);
+
+      const pageSize = 1000; // 分批拉取，避免后端单页限制
+      let page = 0; // 后端 page 参数为 0-based，从 0 开始
+      let total = 0;
+      const allFiles: DatasetFile[] = [];
+
+      while (true) {
+        const { data } = await queryDatasetFilesUsingGet(selectedDataset.id, {
+          page,
+          size: pageSize,
+        });
+
+        const content: DatasetFile[] = (data.content || []).map(
+          (item: DatasetFile) => ({
+            ...item,
+            key: item.id,
+            datasetName: selectedDataset.name,
+          }),
+        );
+
+        if (!content.length) {
+          break;
+        }
+
+        allFiles.push(...content);
+        // 优先用后端的 totalElements，否则用当前累积数
+        total = typeof data.totalElements === "number" ? data.totalElements : allFiles.length;
+
+        // 如果这一页数量小于 pageSize，说明已经拿完；否则继续下一页
+        if (content.length < pageSize) {
+          break;
+        }
+
+        page += 1;
+      }
+
+      const newMap: { [key: string]: DatasetFile } = { ...selectedFilesMap };
+      allFiles.forEach((file) => {
+        if (file && file.id != null) {
+          newMap[String(file.id)] = file;
+        }
+      });
+
+      onSelectedFilesChange(newMap);
+
+      const count = total || allFiles.length;
+      if (count > 0) {
+        message.success(`已选中当前数据集的全部 ${count} 个文件`);
+      } else {
+        message.info("当前数据集下没有可选文件");
+      }
+    } catch (error) {
+      console.error("Failed to select all files in dataset", error);
+      message.error("全选整个数据集失败，请稍后重试");
+    } finally {
+      setSelectingAll(false);
+    }
+  }, [selectedDataset, selectedFilesMap, onSelectedFilesChange]);
+
   const toggleSelectFile = (record: DatasetFile) => {
     if (!selectedFilesMap[record.id]) {
       onSelectedFilesChange({
@@ -245,7 +317,18 @@ const DatasetFileTransfer: React.FC<DatasetFileTransferProps> = ({
         </div>
         <RightOutlined />
         <div className="border-card flex flex-col col-span-12">
-          <div className="border-bottom p-2 font-bold">选择文件</div>
+          <div className="border-bottom p-2 font-bold flex justify-between items-center">
+            <span>选择文件</span>
+            <Button
+              type="link"
+              size="small"
+              onClick={handleSelectAllInDataset}
+              disabled={!selectedDataset}
+              loading={selectingAll}
+            >
+              全选当前数据集
+            </Button>
+          </div>
           <div className="p-2">
             <Input
               placeholder="搜索文件名称..."
@@ -255,7 +338,7 @@ const DatasetFileTransfer: React.FC<DatasetFileTransferProps> = ({
           </div>
           <Table
             scroll={{ y: 400 }}
-            rowKey="id"
+            rowKey={(record) => String(record.id)}
             size="small"
             dataSource={files}
             columns={fileCols.slice(1, fileCols.length)}
@@ -268,7 +351,8 @@ const DatasetFileTransfer: React.FC<DatasetFileTransferProps> = ({
                   current: page,
                   pageSize: nextPageSize,
                 }));
-                fetchFiles({ page, pageSize: nextPageSize }).catch(() => {});
+                // 前端分页是 1-based，后端是 0-based，所以这里传 page - 1
+                fetchFiles({ page: page - 1, pageSize: nextPageSize }).catch(() => {});
               },
             }}
             onRow={(record: DatasetFile) => ({
@@ -277,31 +361,22 @@ const DatasetFileTransfer: React.FC<DatasetFileTransferProps> = ({
             rowSelection={{
               type: "checkbox",
               selectedRowKeys: Object.keys(selectedFilesMap),
+              preserveSelectedRowKeys: true,
 
               // 单选
               onSelect: (record: DatasetFile) => {
                 toggleSelectFile(record);
               },
 
-              // 全选
+              // 全选 - 改为全选整个数据集而不是当前页
               onSelectAll: (selected, selectedRows: DatasetFile[]) => {
                 if (selected) {
-                  // ✔ 全选 -> 将 files 列表全部加入 selectedFilesMap
-                  const newMap: Record<string, DatasetFile> = { ...selectedFilesMap };
-                  selectedRows.forEach((f) => {
-                    newMap[f.id] = f;
-                  });
-                  onSelectedFilesChange(newMap);
+                  // 点击表头“全选”时，改为一键全选当前数据集的全部文件
+                  // 而不是只选中当前页
+                  handleSelectAllInDataset();
                 } else {
-                  // ✘ 取消全选 -> 清空 map
-                  const newMap = { ...selectedFilesMap };
-                  Object.keys(newMap).forEach((id) => {
-                    if (files.some((f) => String(f.id) === id)) {
-                      // 仅移除当前页对应文件
-                      delete newMap[id];
-                    }
-                  });
-                  onSelectedFilesChange(newMap);
+                  // 取消表头“全选”时，清空当前已选文件
+                  onSelectedFilesChange({});
                 }
               },
 

frontend/src/hooks/useSliceUpload.tsx

@@ -32,6 +32,7 @@ export function useFileSliceUpload(
       size: 0,
       updateEvent: detail.updateEvent,
       hasArchive: detail.hasArchive,
+      prefix: detail.prefix,
     };
     taskListRef.current = [task, ...taskListRef.current];
 
@@ -55,7 +56,14 @@ export function useFileSliceUpload(
     if (task.isCancel && task.cancelFn) {
       task.cancelFn();
     }
-    if (task.updateEvent) window.dispatchEvent(new Event(task.updateEvent));
+    if (task.updateEvent) {
+      // 携带前缀信息，便于刷新后仍停留在当前目录
+      window.dispatchEvent(
+        new CustomEvent(task.updateEvent, {
+          detail: { prefix: (task as any).prefix },
+        })
+      );
+    }
     if (showTaskCenter) {
       window.dispatchEvent(
         new CustomEvent("show:task-popover", { detail: { show: false } })
@@ -109,12 +117,15 @@ export function useFileSliceUpload(
   }
 
   async function uploadFile({ task, files, totalSize }) {
+    console.log('[useSliceUpload] Calling preUpload with prefix:', task.prefix);
     const { data: reqId } = await preUpload(task.key, {
       totalFileNum: files.length,
       totalSize,
       datasetId: task.key,
       hasArchive: task.hasArchive,
+      prefix: task.prefix,
     });
+    console.log('[useSliceUpload] PreUpload response reqId:', reqId);
 
     const newTask: TaskItem = {
       ...task,

frontend/src/pages/DataAnnotation/AutoAnnotation/AutoAnnotation.tsx

@@ -0,0 +1,302 @@
+import { useState, useEffect } from "react";
+import { Card, Button, Table, message, Modal, Tag, Progress, Space, Tooltip } from "antd";
+import {
+	PlusOutlined,
+	DeleteOutlined,
+	DownloadOutlined,
+	ReloadOutlined,
+	EyeOutlined,
+} from "@ant-design/icons";
+import type { ColumnType } from "antd/es/table";
+import type { AutoAnnotationTask, AutoAnnotationStatus } from "../annotation.model";
+import {
+	queryAutoAnnotationTasksUsingGet,
+	deleteAutoAnnotationTaskByIdUsingDelete,
+	downloadAutoAnnotationResultUsingGet,
+} from "../annotation.api";
+import CreateAutoAnnotationDialog from "./components/CreateAutoAnnotationDialog";
+
+const STATUS_COLORS: Record<AutoAnnotationStatus, string> = {
+	pending: "default",
+	running: "processing",
+	completed: "success",
+	failed: "error",
+	cancelled: "default",
+};
+
+const STATUS_LABELS: Record<AutoAnnotationStatus, string> = {
+	pending: "等待中",
+	running: "处理中",
+	completed: "已完成",
+	failed: "失败",
+	cancelled: "已取消",
+};
+
+const MODEL_SIZE_LABELS: Record<string, string> = {
+	n: "YOLOv8n (最快)",
+	s: "YOLOv8s",
+	m: "YOLOv8m",
+	l: "YOLOv8l (推荐)",
+	x: "YOLOv8x (最精确)",
+};
+
+export default function AutoAnnotation() {
+	const [loading, setLoading] = useState(false);
+	const [tasks, setTasks] = useState<AutoAnnotationTask[]>([]);
+	const [showCreateDialog, setShowCreateDialog] = useState(false);
+	const [selectedRowKeys, setSelectedRowKeys] = useState<string[]>([]);
+
+	useEffect(() => {
+		fetchTasks();
+		const interval = setInterval(() => {
+			fetchTasks(true);
+		}, 3000);
+		return () => clearInterval(interval);
+	}, []);
+
+	const fetchTasks = async (silent = false) => {
+		if (!silent) setLoading(true);
+		try {
+			const response = await queryAutoAnnotationTasksUsingGet();
+			setTasks(response.data || response || []);
+		} catch (error) {
+			console.error("Failed to fetch auto annotation tasks:", error);
+			if (!silent) message.error("获取任务列表失败");
+		} finally {
+			if (!silent) setLoading(false);
+		}
+	};
+
+	const handleDelete = (task: AutoAnnotationTask) => {
+		Modal.confirm({
+			title: `确认删除自动标注任务「${task.name}」吗？`,
+			content: "删除任务后，已生成的标注结果不会被删除。",
+			okText: "删除",
+			okType: "danger",
+			cancelText: "取消",
+			onOk: async () => {
+				try {
+					await deleteAutoAnnotationTaskByIdUsingDelete(task.id);
+					message.success("任务删除成功");
+					fetchTasks();
+					setSelectedRowKeys((keys) => keys.filter((k) => k !== task.id));
+				} catch (error) {
+					console.error(error);
+					message.error("删除失败，请稍后重试");
+				}
+			},
+		});
+	};
+
+	const handleDownload = async (task: AutoAnnotationTask) => {
+		try {
+			message.loading("正在准备下载...", 0);
+			await downloadAutoAnnotationResultUsingGet(task.id);
+			message.destroy();
+			message.success("下载已开始");
+		} catch (error) {
+			console.error(error);
+			message.destroy();
+			message.error("下载失败");
+		}
+	};
+
+	const handleViewResult = (task: AutoAnnotationTask) => {
+		if (task.outputPath) {
+			Modal.info({
+				title: "标注结果路径",
+				content: (
+					<div>
+						<p>输出路径：{task.outputPath}</p>
+						<p>检测对象数：{task.detectedObjects}</p>
+						<p>
+							处理图片数：{task.processedImages} / {task.totalImages}
+						</p>
+					</div>
+				),
+			});
+		}
+	};
+
+	const columns: ColumnType<AutoAnnotationTask>[] = [
+		{ title: "任务名称", dataIndex: "name", key: "name", width: 200 },
+		{
+			title: "数据集",
+			dataIndex: "datasetName",
+			key: "datasetName",
+			width: 220,
+			render: (_: any, record: AutoAnnotationTask) => {
+				const list =
+					record.sourceDatasets && record.sourceDatasets.length > 0
+						? record.sourceDatasets
+						: record.datasetName
+						? [record.datasetName]
+						: [];
+
+				if (list.length === 0) return "-";
+
+				const text = list.join("，");
+				return (
+					<Tooltip title={text}>
+						<span>{text}</span>
+					</Tooltip>
+				);
+			},
+		},
+		{
+			title: "模型",
+			dataIndex: ["config", "modelSize"],
+			key: "modelSize",
+			width: 120,
+			render: (size: string) => MODEL_SIZE_LABELS[size] || size,
+		},
+		{
+			title: "置信度",
+			dataIndex: ["config", "confThreshold"],
+			key: "confThreshold",
+			width: 100,
+			render: (threshold: number) => `${(threshold * 100).toFixed(0)}%`,
+		},
+		{
+			title: "目标类别",
+			dataIndex: ["config", "targetClasses"],
+			key: "targetClasses",
+			width: 120,
+			render: (classes: number[]) => (
+				<Tooltip
+					title={classes.length > 0 ? classes.join(", ") : "全部类别"}
+				>
+					<span>
+						{classes.length > 0
+							? `${classes.length} 个类别`
+							: "全部类别"}
+					</span>
+				</Tooltip>
+			),
+		},
+		{
+			title: "状态",
+			dataIndex: "status",
+			key: "status",
+			width: 100,
+			render: (status: AutoAnnotationStatus) => (
+				<Tag color={STATUS_COLORS[status]}>{STATUS_LABELS[status]}</Tag>
+			),
+		},
+		{
+			title: "进度",
+			dataIndex: "progress",
+			key: "progress",
+			width: 150,
+			render: (progress: number, record: AutoAnnotationTask) => (
+				<div>
+					<Progress percent={progress} size="small" />
+					<div style={{ fontSize: "12px", color: "#999" }}>
+						{record.processedImages} / {record.totalImages}
+					</div>
+				</div>
+			),
+		},
+		{
+			title: "检测对象数",
+			dataIndex: "detectedObjects",
+			key: "detectedObjects",
+			width: 100,
+			render: (count: number) => count.toLocaleString(),
+		},
+		{
+			title: "创建时间",
+			dataIndex: "createdAt",
+			key: "createdAt",
+			width: 150,
+			render: (time: string) => new Date(time).toLocaleString(),
+		},
+		{
+			title: "操作",
+			key: "actions",
+			width: 180,
+			fixed: "right",
+			render: (_: any, record: AutoAnnotationTask) => (
+				<Space size="small">
+					{record.status === "completed" && (
+						<>
+							<Tooltip title="查看结果">
+								<Button
+									type="link"
+									size="small"
+									icon={<EyeOutlined />}
+									onClick={() => handleViewResult(record)}
+								/>
+							</Tooltip>
+							<Tooltip title="下载结果">
+								<Button
+									type="link"
+									size="small"
+									icon={<DownloadOutlined />}
+									onClick={() => handleDownload(record)}
+								/>
+							</Tooltip>
+						</>
+					)}
+					<Tooltip title="删除">
+						<Button
+							type="link"
+							size="small"
+							danger
+							icon={<DeleteOutlined />}
+							onClick={() => handleDelete(record)}
+						/>
+					</Tooltip>
+				</Space>
+			),
+		},
+	];
+
+	return (
+		<div>
+			<Card
+				title="自动标注任务"
+				extra={
+					<Space>
+						<Button
+							type="primary"
+							icon={<PlusOutlined />}
+							onClick={() => setShowCreateDialog(true)}
+						>
+							创建任务
+						</Button>
+						<Button
+							icon={<ReloadOutlined />}
+							loading={loading}
+							onClick={() => fetchTasks()}
+						>
+							刷新
+						</Button>
+					</Space>
+				}
+			>
+				<Table
+					rowKey="id"
+					loading={loading}
+					columns={columns}
+					dataSource={tasks}
+					rowSelection={{
+						selectedRowKeys,
+						onChange: (keys) => setSelectedRowKeys(keys as string[]),
+					}}
+					pagination={{ pageSize: 10 }}
+					scroll={{ x: 1000 }}
+				/>
+			</Card>
+
+			<CreateAutoAnnotationDialog
+				visible={showCreateDialog}
+				onCancel={() => setShowCreateDialog(false)}
+				onSuccess={() => {
+					setShowCreateDialog(false);
+					fetchTasks();
+				}}
+			/>
+		</div>
+	);
+}

frontend/src/pages/DataAnnotation/AutoAnnotation/components/CreateAutoAnnotationDialog.tsx

@@ -0,0 +1,286 @@
+import { useState, useEffect } from "react";
+import { Modal, Form, Input, Select, Slider, message, Checkbox } from "antd";
+import { createAutoAnnotationTaskUsingPost } from "../../annotation.api";
+import { queryDatasetsUsingGet } from "@/pages/DataManagement/dataset.api";
+import { mapDataset } from "@/pages/DataManagement/dataset.const";
+import { DatasetType, type DatasetFile, type Dataset } from "@/pages/DataManagement/dataset.model";
+import DatasetFileTransfer from "@/components/business/DatasetFileTransfer";
+
+const { Option } = Select;
+
+interface CreateAutoAnnotationDialogProps {
+	visible: boolean;
+	onCancel: () => void;
+	onSuccess: () => void;
+}
+
+const COCO_CLASSES = [
+	{ id: 0, name: "person", label: "人" },
+	{ id: 1, name: "bicycle", label: "自行车" },
+	{ id: 2, name: "car", label: "汽车" },
+	{ id: 3, name: "motorcycle", label: "摩托车" },
+	{ id: 4, name: "airplane", label: "飞机" },
+	{ id: 5, name: "bus", label: "公交车" },
+	{ id: 6, name: "train", label: "火车" },
+	{ id: 7, name: "truck", label: "卡车" },
+	{ id: 8, name: "boat", label: "船" },
+	{ id: 9, name: "traffic light", label: "红绿灯" },
+	{ id: 10, name: "fire hydrant", label: "消防栓" },
+	{ id: 11, name: "stop sign", label: "停止标志" },
+	{ id: 12, name: "parking meter", label: "停车计时器" },
+	{ id: 13, name: "bench", label: "长椅" },
+	{ id: 14, name: "bird", label: "鸟" },
+	{ id: 15, name: "cat", label: "猫" },
+	{ id: 16, name: "dog", label: "狗" },
+	{ id: 17, name: "horse", label: "马" },
+	{ id: 18, name: "sheep", label: "羊" },
+	{ id: 19, name: "cow", label: "牛" },
+	{ id: 20, name: "elephant", label: "大象" },
+	{ id: 21, name: "bear", label: "熊" },
+	{ id: 22, name: "zebra", label: "斑马" },
+	{ id: 23, name: "giraffe", label: "长颈鹿" },
+	{ id: 24, name: "backpack", label: "背包" },
+	{ id: 25, name: "umbrella", label: "雨伞" },
+	{ id: 26, name: "handbag", label: "手提包" },
+	{ id: 27, name: "tie", label: "领带" },
+	{ id: 28, name: "suitcase", label: "行李箱" },
+	{ id: 29, name: "frisbee", label: "飞盘" },
+	{ id: 30, name: "skis", label: "滑雪板" },
+	{ id: 31, name: "snowboard", label: "滑雪板" },
+	{ id: 32, name: "sports ball", label: "球类" },
+	{ id: 33, name: "kite", label: "风筝" },
+	{ id: 34, name: "baseball bat", label: "棒球棒" },
+	{ id: 35, name: "baseball glove", label: "棒球手套" },
+	{ id: 36, name: "skateboard", label: "滑板" },
+	{ id: 37, name: "surfboard", label: "冲浪板" },
+	{ id: 38, name: "tennis racket", label: "网球拍" },
+	{ id: 39, name: "bottle", label: "瓶子" },
+	{ id: 40, name: "wine glass", label: "酒杯" },
+	{ id: 41, name: "cup", label: "杯子" },
+	{ id: 42, name: "fork", label: "叉子" },
+	{ id: 43, name: "knife", label: "刀" },
+	{ id: 44, name: "spoon", label: "勺子" },
+	{ id: 45, name: "bowl", label: "碗" },
+	{ id: 46, name: "banana", label: "香蕉" },
+	{ id: 47, name: "apple", label: "苹果" },
+	{ id: 48, name: "sandwich", label: "三明治" },
+	{ id: 49, name: "orange", label: "橙子" },
+	{ id: 50, name: "broccoli", label: "西兰花" },
+	{ id: 51, name: "carrot", label: "胡萝卜" },
+	{ id: 52, name: "hot dog", label: "热狗" },
+	{ id: 53, name: "pizza", label: "披萨" },
+	{ id: 54, name: "donut", label: "甜甜圈" },
+	{ id: 55, name: "cake", label: "蛋糕" },
+	{ id: 56, name: "chair", label: "椅子" },
+	{ id: 57, name: "couch", label: "沙发" },
+	{ id: 58, name: "potted plant", label: "盆栽" },
+	{ id: 59, name: "bed", label: "床" },
+	{ id: 60, name: "dining table", label: "餐桌" },
+	{ id: 61, name: "toilet", label: "马桶" },
+	{ id: 62, name: "tv", label: "电视" },
+	{ id: 63, name: "laptop", label: "笔记本电脑" },
+	{ id: 64, name: "mouse", label: "鼠标" },
+	{ id: 65, name: "remote", label: "遥控器" },
+	{ id: 66, name: "keyboard", label: "键盘" },
+	{ id: 67, name: "cell phone", label: "手机" },
+	{ id: 68, name: "microwave", label: "微波炉" },
+	{ id: 69, name: "oven", label: "烤箱" },
+	{ id: 70, name: "toaster", label: "烤面包机" },
+	{ id: 71, name: "sink", label: "水槽" },
+	{ id: 72, name: "refrigerator", label: "冰箱" },
+	{ id: 73, name: "book", label: "书" },
+	{ id: 74, name: "clock", label: "钟表" },
+	{ id: 75, name: "vase", label: "花瓶" },
+	{ id: 76, name: "scissors", label: "剪刀" },
+	{ id: 77, name: "teddy bear", label: "玩具熊" },
+	{ id: 78, name: "hair drier", label: "吹风机" },
+	{ id: 79, name: "toothbrush", label: "牙刷" },
+];
+
+export default function CreateAutoAnnotationDialog({
+	visible,
+	onCancel,
+	onSuccess,
+}: CreateAutoAnnotationDialogProps) {
+	const [form] = Form.useForm();
+	const [loading, setLoading] = useState(false);
+	const [datasets, setDatasets] = useState<any[]>([]);
+	const [selectAllClasses, setSelectAllClasses] = useState(true);
+	const [selectedFilesMap, setSelectedFilesMap] = useState<Record<string, DatasetFile>>({});
+	const [selectedDataset, setSelectedDataset] = useState<Dataset | null>(null);
+	const [imageFileCount, setImageFileCount] = useState(0);
+
+	useEffect(() => {
+		if (visible) {
+			fetchDatasets();
+			form.resetFields();
+			form.setFieldsValue({
+				modelSize: "l",
+				confThreshold: 0.7,
+				targetClasses: [],
+			});
+		}
+	}, [visible, form]);
+
+	const fetchDatasets = async () => {
+		try {
+			const { data } = await queryDatasetsUsingGet({
+				page: 0,
+				pageSize: 1000,
+			});
+			const imageDatasets = (data.content || [])
+				.map(mapDataset)
+				.filter((ds: any) => ds.datasetType === DatasetType.IMAGE);
+			setDatasets(imageDatasets);
+		} catch (error) {
+			console.error("Failed to fetch datasets:", error);
+			message.error("获取数据集列表失败");
+		}
+	};
+
+	useEffect(() => {
+		const imageExtensions = [".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff", ".webp"];
+		const count = Object.values(selectedFilesMap).filter((file) => {
+			const ext = file.fileName?.toLowerCase().match(/\.[^.]+$/)?.[0] || "";
+			return imageExtensions.includes(ext);
+		}).length;
+		setImageFileCount(count);
+	}, [selectedFilesMap]);
+
+	const handleSubmit = async () => {
+		try {
+			const values = await form.validateFields();
+
+			if (imageFileCount === 0) {
+				message.error("请至少选择一个图像文件");
+				return;
+			}
+
+			setLoading(true);
+
+			const imageExtensions = [".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff", ".webp"];
+			const imageFileIds = Object.values(selectedFilesMap)
+				.filter((file) => {
+					const ext = file.fileName?.toLowerCase().match(/\.[^.]+$/)?.[0] || "";
+					return imageExtensions.includes(ext);
+				})
+				.map((file) => file.id);
+
+			const payload = {
+				name: values.name,
+				datasetId: values.datasetId,
+				fileIds: imageFileIds,
+				config: {
+					modelSize: values.modelSize,
+					confThreshold: values.confThreshold,
+					targetClasses: selectAllClasses ? [] : values.targetClasses || [],
+					outputDatasetName: values.outputDatasetName || undefined,
+				},
+			};
+
+			await createAutoAnnotationTaskUsingPost(payload);
+			message.success("自动标注任务创建成功");
+			onSuccess();
+		} catch (error: any) {
+			if (error.errorFields) return;
+			console.error("Failed to create auto annotation task:", error);
+			message.error(error.message || "创建任务失败");
+		} finally {
+			setLoading(false);
+		}
+	};
+
+	const handleClassSelectionChange = (checked: boolean) => {
+		setSelectAllClasses(checked);
+		if (checked) {
+			form.setFieldsValue({ targetClasses: [] });
+		}
+	};
+
+	return (
+		<Modal
+			title="创建自动标注任务"
+			open={visible}
+			onCancel={onCancel}
+			onOk={handleSubmit}
+			confirmLoading={loading}
+			width={600}
+			destroyOnClose
+		>
+			<Form form={form} layout="vertical" preserve={false}>
+				<Form.Item
+					name="name"
+					label="任务名称"
+					rules={[
+						{ required: true, message: "请输入任务名称" },
+						{ max: 100, message: "任务名称不能超过100个字符" },
+					]}
+				>
+					<Input placeholder="请输入任务名称" />
+				</Form.Item>
+
+				<Form.Item label="选择数据集和图像文件" required>
+					<DatasetFileTransfer
+						open
+						selectedFilesMap={selectedFilesMap}
+						onSelectedFilesChange={setSelectedFilesMap}
+						onDatasetSelect={(dataset) => {
+							setSelectedDataset(dataset);
+							form.setFieldsValue({ datasetId: dataset?.id ?? "" });
+						}}
+						datasetTypeFilter={DatasetType.IMAGE}
+					/>
+					{selectedDataset && (
+						<div className="mt-2 p-2 bg-blue-50 rounded border border-blue-200 text-xs">
+							当前数据集：<span className="font-medium">{selectedDataset.name}</span> - 已选择
+							<span className="font-medium text-blue-600"> {imageFileCount} </span>个图像文件
+						</div>
+					)}
+				</Form.Item>
+
+				<Form.Item hidden name="datasetId" rules={[{ required: true, message: "请选择数据集" }]}>
+					<Input type="hidden" />
+				</Form.Item>
+
+				<Form.Item name="modelSize" label="模型规模" rules={[{ required: true, message: "请选择模型规模" }]}>
+					<Select>
+						<Option value="n">YOLOv8n (最快)</Option>
+						<Option value="s">YOLOv8s</Option>
+						<Option value="m">YOLOv8m</Option>
+						<Option value="l">YOLOv8l (推荐)</Option>
+						<Option value="x">YOLOv8x (最精确)</Option>
+					</Select>
+				</Form.Item>
+
+				<Form.Item
+					name="confThreshold"
+					label="置信度阈值"
+					rules={[{ required: true, message: "请选择置信度阈值" }]}
+				>
+					<Slider min={0.1} max={0.9} step={0.05} tooltip={{ formatter: (v) => `${(v || 0) * 100}%` }} />
+				</Form.Item>
+
+				<Form.Item label="目标类别">
+					<Checkbox checked={selectAllClasses} onChange={(e) => handleClassSelectionChange(e.target.checked)}>
+						选中所有类别
+					</Checkbox>
+					{!selectAllClasses && (
+						<Form.Item name="targetClasses" noStyle>
+							<Select mode="multiple" placeholder="选择目标类别" style={{ marginTop: 8 }}>
+								{COCO_CLASSES.map((cls) => (
+									<Option key={cls.id} value={cls.id}>
+										{cls.label} ({cls.name})
+									</Option>
+								))}
+							</Select>
+						</Form.Item>
+					)}
+				</Form.Item>
+
+				<Form.Item name="outputDatasetName" label="输出数据集名称 (可选)">
+					<Input placeholder="留空则将结果写入原数据集的标签中" />
+				</Form.Item>
+			</Form>
+		</Modal>
+	);
+}

frontend/src/pages/DataAnnotation/AutoAnnotation/index.ts

@@ -0,0 +1 @@
+export { default } from "./AutoAnnotation";

frontend/src/pages/DataAnnotation/Create/components/CreateAnnotationTaskDialog.tsx

@@ -1,12 +1,102 @@
 import { queryDatasetsUsingGet } from "@/pages/DataManagement/dataset.api";
 import { mapDataset } from "@/pages/DataManagement/dataset.const";
-import { Button, Form, Input, Modal, Select, message } from "antd";
+import { Button, Form, Input, Modal, Select, message, Tabs, Slider, Checkbox } from "antd";
 import TextArea from "antd/es/input/TextArea";
 import { useEffect, useState } from "react";
-import { createAnnotationTaskUsingPost, queryAnnotationTemplatesUsingGet } from "../../annotation.api";
-import { Dataset } from "@/pages/DataManagement/dataset.model";
+import {
+  createAnnotationTaskUsingPost,
+  queryAnnotationTemplatesUsingGet,
+  createAutoAnnotationTaskUsingPost,
+} from "../../annotation.api";
+import DatasetFileTransfer from "@/components/business/DatasetFileTransfer";
+import { DatasetType, type Dataset, type DatasetFile } from "@/pages/DataManagement/dataset.model";
 import type { AnnotationTemplate } from "../../annotation.model";
 
+const { Option } = Select;
+
+const COCO_CLASSES = [
+  { id: 0, name: "person", label: "人" },
+  { id: 1, name: "bicycle", label: "自行车" },
+  { id: 2, name: "car", label: "汽车" },
+  { id: 3, name: "motorcycle", label: "摩托车" },
+  { id: 4, name: "airplane", label: "飞机" },
+  { id: 5, name: "bus", label: "公交车" },
+  { id: 6, name: "train", label: "火车" },
+  { id: 7, name: "truck", label: "卡车" },
+  { id: 8, name: "boat", label: "船" },
+  { id: 9, name: "traffic light", label: "红绿灯" },
+  { id: 10, name: "fire hydrant", label: "消防栓" },
+  { id: 11, name: "stop sign", label: "停止标志" },
+  { id: 12, name: "parking meter", label: "停车计时器" },
+  { id: 13, name: "bench", label: "长椅" },
+  { id: 14, name: "bird", label: "鸟" },
+  { id: 15, name: "cat", label: "猫" },
+  { id: 16, name: "dog", label: "狗" },
+  { id: 17, name: "horse", label: "马" },
+  { id: 18, name: "sheep", label: "羊" },
+  { id: 19, name: "cow", label: "牛" },
+  { id: 20, name: "elephant", label: "大象" },
+  { id: 21, name: "bear", label: "熊" },
+  { id: 22, name: "zebra", label: "斑马" },
+  { id: 23, name: "giraffe", label: "长颈鹿" },
+  { id: 24, name: "backpack", label: "背包" },
+  { id: 25, name: "umbrella", label: "雨伞" },
+  { id: 26, name: "handbag", label: "手提包" },
+  { id: 27, name: "tie", label: "领带" },
+  { id: 28, name: "suitcase", label: "行李箱" },
+  { id: 29, name: "frisbee", label: "飞盘" },
+  { id: 30, name: "skis", label: "滑雪板" },
+  { id: 31, name: "snowboard", label: "滑雪板" },
+  { id: 32, name: "sports ball", label: "球类" },
+  { id: 33, name: "kite", label: "风筝" },
+  { id: 34, name: "baseball bat", label: "棒球棒" },
+  { id: 35, name: "baseball glove", label: "棒球手套" },
+  { id: 36, name: "skateboard", label: "滑板" },
+  { id: 37, name: "surfboard", label: "冲浪板" },
+  { id: 38, name: "tennis racket", label: "网球拍" },
+  { id: 39, name: "bottle", label: "瓶子" },
+  { id: 40, name: "wine glass", label: "酒杯" },
+  { id: 41, name: "cup", label: "杯子" },
+  { id: 42, name: "fork", label: "叉子" },
+  { id: 43, name: "knife", label: "刀" },
+  { id: 44, name: "spoon", label: "勺子" },
+  { id: 45, name: "bowl", label: "碗" },
+  { id: 46, name: "banana", label: "香蕉" },
+  { id: 47, name: "apple", label: "苹果" },
+  { id: 48, name: "sandwich", label: "三明治" },
+  { id: 49, name: "orange", label: "橙子" },
+  { id: 50, name: "broccoli", label: "西兰花" },
+  { id: 51, name: "carrot", label: "胡萝卜" },
+  { id: 52, name: "hot dog", label: "热狗" },
+  { id: 53, name: "pizza", label: "披萨" },
+  { id: 54, name: "donut", label: "甜甜圈" },
+  { id: 55, name: "cake", label: "蛋糕" },
+  { id: 56, name: "chair", label: "椅子" },
+  { id: 57, name: "couch", label: "沙发" },
+  { id: 58, name: "potted plant", label: "盆栽" },
+  { id: 59, name: "bed", label: "床" },
+  { id: 60, name: "dining table", label: "餐桌" },
+  { id: 61, name: "toilet", label: "马桶" },
+  { id: 62, name: "tv", label: "电视" },
+  { id: 63, name: "laptop", label: "笔记本电脑" },
+  { id: 64, name: "mouse", label: "鼠标" },
+  { id: 65, name: "remote", label: "遥控器" },
+  { id: 66, name: "keyboard", label: "键盘" },
+  { id: 67, name: "cell phone", label: "手机" },
+  { id: 68, name: "microwave", label: "微波炉" },
+  { id: 69, name: "oven", label: "烤箱" },
+  { id: 70, name: "toaster", label: "烤面包机" },
+  { id: 71, name: "sink", label: "水槽" },
+  { id: 72, name: "refrigerator", label: "冰箱" },
+  { id: 73, name: "book", label: "书" },
+  { id: 74, name: "clock", label: "钟表" },
+  { id: 75, name: "vase", label: "花瓶" },
+  { id: 76, name: "scissors", label: "剪刀" },
+  { id: 77, name: "teddy bear", label: "玩具熊" },
+  { id: 78, name: "hair drier", label: "吹风机" },
+  { id: 79, name: "toothbrush", label: "牙刷" },
+];
+
 export default function CreateAnnotationTask({
   open,
   onClose,
@@ -16,11 +106,18 @@ export default function CreateAnnotationTask({
   onClose: () => void;
   onRefresh: () => void;
 }) {
-  const [form] = Form.useForm();
+  const [manualForm] = Form.useForm();
+  const [autoForm] = Form.useForm();
   const [datasets, setDatasets] = useState<Dataset[]>([]);
   const [templates, setTemplates] = useState<AnnotationTemplate[]>([]);
   const [submitting, setSubmitting] = useState(false);
   const [nameManuallyEdited, setNameManuallyEdited] = useState(false);
+  const [activeMode, setActiveMode] = useState<"manual" | "auto">("manual");
+
+  const [selectAllClasses, setSelectAllClasses] = useState(true);
+  const [selectedFilesMap, setSelectedFilesMap] = useState<Record<string, DatasetFile>>({});
+  const [selectedDataset, setSelectedDataset] = useState<Dataset | null>(null);
+  const [imageFileCount, setImageFileCount] = useState(0);
 
   useEffect(() => {
     if (!open) return;
@@ -59,14 +156,29 @@ export default function CreateAnnotationTask({
   // Reset form and manual-edit flag when modal opens
   useEffect(() => {
     if (open) {
-      form.resetFields();
+      manualForm.resetFields();
+      autoForm.resetFields();
       setNameManuallyEdited(false);
+      setActiveMode("manual");
+      setSelectAllClasses(true);
+      setSelectedFilesMap({});
+      setSelectedDataset(null);
+      setImageFileCount(0);
     }
-  }, [open, form]);
+  }, [open, manualForm, autoForm]);
 
-  const handleSubmit = async () => {
+  useEffect(() => {
+    const imageExtensions = [".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff", ".webp"];
+    const count = Object.values(selectedFilesMap).filter((file) => {
+      const ext = file.fileName?.toLowerCase().match(/\.[^.]+$/)?.[0] || "";
+      return imageExtensions.includes(ext);
+    }).length;
+    setImageFileCount(count);
+  }, [selectedFilesMap]);
+
+  const handleManualSubmit = async () => {
     try {
-      const values = await form.validateFields();
+      const values = await manualForm.validateFields();
       setSubmitting(true);
       // Send templateId instead of labelingConfig
       const requestData = {
@@ -88,6 +200,58 @@ export default function CreateAnnotationTask({
     }
   };
 
+  const handleAutoSubmit = async () => {
+    try {
+      const values = await autoForm.validateFields();
+
+      if (imageFileCount === 0) {
+        message.error("请至少选择一个图像文件");
+        return;
+      }
+
+      setSubmitting(true);
+
+      const imageExtensions = [".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff", ".webp"];
+      const imageFileIds = Object.values(selectedFilesMap)
+        .filter((file) => {
+          const ext = file.fileName?.toLowerCase().match(/\.[^.]+$/)?.[0] || "";
+          return imageExtensions.includes(ext);
+        })
+        .map((file) => file.id);
+
+      const payload = {
+        name: values.name,
+        datasetId: values.datasetId,
+        fileIds: imageFileIds,
+        config: {
+          modelSize: values.modelSize,
+          confThreshold: values.confThreshold,
+          targetClasses: selectAllClasses ? [] : values.targetClasses || [],
+          outputDatasetName: values.outputDatasetName || undefined,
+        },
+      };
+
+      await createAutoAnnotationTaskUsingPost(payload);
+      message.success("自动标注任务创建成功");
+      // 触发上层刷新自动标注任务列表
+      (onRefresh as any)?.("auto");
+      onClose();
+    } catch (error: any) {
+      if (error.errorFields) return;
+      console.error("Failed to create auto annotation task:", error);
+      message.error(error.message || "创建自动标注任务失败");
+    } finally {
+      setSubmitting(false);
+    }
+  };
+
+  const handleClassSelectionChange = (checked: boolean) => {
+    setSelectAllClasses(checked);
+    if (checked) {
+      autoForm.setFieldsValue({ targetClasses: [] });
+    }
+  };
+
   return (
     <Modal
       open={open}
@@ -98,14 +262,26 @@ export default function CreateAnnotationTask({
           <Button onClick={onClose} disabled={submitting}>
             取消
           </Button>
-          <Button type="primary" onClick={handleSubmit} loading={submitting}>
+          <Button
+            type="primary"
+            onClick={activeMode === "manual" ? handleManualSubmit : handleAutoSubmit}
+            loading={submitting}
+          >
             确定
           </Button>
         </>
       }
       width={800}
     >
-      <Form form={form} layout="vertical">
+      <Tabs
+        activeKey={activeMode}
+        onChange={(key) => setActiveMode(key as "manual" | "auto")}
+        items={[
+          {
+            key: "manual",
+            label: "手动标注",
+            children: (
+              <Form form={manualForm} layout="vertical">
                 {/* 数据集 与 标注工程名称 并排显示（数据集在左） */}
                 <div className="grid grid-cols-2 gap-4">
                   <Form.Item
@@ -134,7 +310,11 @@ export default function CreateAnnotationTask({
                         if (!nameManuallyEdited) {
                           const ds = datasets.find((d) => d.id === value);
                           if (ds) {
-                    form.setFieldsValue({ name: ds.name });
+                            let defaultName = ds.name || "";
+                            if (defaultName.length < 3) {
+                              defaultName = `${defaultName}-标注`;
+                            }
+                            manualForm.setFieldsValue({ name: defaultName });
                           }
                         }
                       }}
@@ -144,7 +324,22 @@ export default function CreateAnnotationTask({
                   <Form.Item
                     label="标注工程名称"
                     name="name"
-            rules={[{ required: true, message: "请输入任务名称" }]}
+                    rules={[
+                      {
+                        validator: (_rule, value) => {
+                          const trimmed = (value || "").trim();
+                          if (!trimmed) {
+                            return Promise.reject(new Error("请输入任务名称"));
+                          }
+                          if (trimmed.length < 3) {
+                            return Promise.reject(
+                              new Error("任务名称至少需要 3 个字符（不含首尾空格，Label Studio 限制）"),
+                            );
+                          }
+                          return Promise.resolve();
+                        },
+                      },
+                    ]}
                   >
                     <Input
                       placeholder="输入标注工程名称"
@@ -187,6 +382,108 @@ export default function CreateAnnotationTask({
                   />
                 </Form.Item>
               </Form>
+            ),
+          },
+          {
+            key: "auto",
+            label: "自动标注",
+            children: (
+              <Form form={autoForm} layout="vertical" preserve={false}>
+                <Form.Item
+                  name="name"
+                  label="任务名称"
+                  rules={[
+                    { required: true, message: "请输入任务名称" },
+                    { max: 100, message: "任务名称不能超过100个字符" },
+                  ]}
+                >
+                  <Input placeholder="请输入任务名称" />
+                </Form.Item>
+
+                <Form.Item label="选择数据集和图像文件" required>
+                  <DatasetFileTransfer
+                    open
+                    selectedFilesMap={selectedFilesMap}
+                    onSelectedFilesChange={setSelectedFilesMap}
+                    onDatasetSelect={(dataset) => {
+                      setSelectedDataset(dataset as Dataset | null);
+                      autoForm.setFieldsValue({ datasetId: dataset?.id ?? "" });
+                    }}
+                    datasetTypeFilter={DatasetType.IMAGE}
+                  />
+                  {selectedDataset && (
+                    <div className="mt-2 p-2 bg-blue-50 rounded border border-blue-200 text-xs">
+                      当前数据集：<span className="font-medium">{selectedDataset.name}</span> - 已选择
+                      <span className="font-medium text-blue-600"> {imageFileCount} </span>个图像文件
+                    </div>
+                  )}
+                </Form.Item>
+
+                <Form.Item
+                  hidden
+                  name="datasetId"
+                  rules={[{ required: true, message: "请选择数据集" }]}
+                >
+                  <Input type="hidden" />
+                </Form.Item>
+
+                <Form.Item
+                  name="modelSize"
+                  label="模型规模"
+                  rules={[{ required: true, message: "请选择模型规模" }]}
+                  initialValue="l"
+                >
+                  <Select>
+                    <Option value="n">YOLOv8n (最快)</Option>
+                    <Option value="s">YOLOv8s</Option>
+                    <Option value="m">YOLOv8m</Option>
+                    <Option value="l">YOLOv8l (推荐)</Option>
+                    <Option value="x">YOLOv8x (最精确)</Option>
+                  </Select>
+                </Form.Item>
+
+                <Form.Item
+                  name="confThreshold"
+                  label="置信度阈值"
+                  rules={[{ required: true, message: "请选择置信度阈值" }]}
+                  initialValue={0.7}
+                >
+                  <Slider
+                    min={0.1}
+                    max={0.9}
+                    step={0.05}
+                    tooltip={{ formatter: (v) => `${(v || 0) * 100}%` }}
+                  />
+                </Form.Item>
+
+                <Form.Item label="目标类别">
+                  <Checkbox
+                    checked={selectAllClasses}
+                    onChange={(e) => handleClassSelectionChange(e.target.checked)}
+                  >
+                    选中所有类别
+                  </Checkbox>
+                  {!selectAllClasses && (
+                    <Form.Item name="targetClasses" noStyle>
+                      <Select mode="multiple" placeholder="选择目标类别" style={{ marginTop: 8 }}>
+                        {COCO_CLASSES.map((cls) => (
+                          <Option key={cls.id} value={cls.id}>
+                            {cls.label} ({cls.name})
+                          </Option>
+                        ))}
+                      </Select>
+                    </Form.Item>
+                  )}
+                </Form.Item>
+
+                <Form.Item name="outputDatasetName" label="输出数据集名称 (可选)">
+                  <Input placeholder="留空则将结果写入原数据集的标签中" />
+                </Form.Item>
+              </Form>
+            ),
+          },
+        ]}
+      />
     </Modal>
   );
 }

frontend/src/pages/DataAnnotation/Home/DataAnnotation.tsx

@@ -1,5 +1,5 @@
 import { useState, useEffect } from "react";
-import { Card, Button, Table, message, Modal, Tabs } from "antd";
+import { Card, Button, Table, message, Modal, Tabs, Tag, Progress, Tooltip } from "antd";
 import {
   PlusOutlined,
   EditOutlined,
@@ -11,9 +11,11 @@ import CardView from "@/components/CardView";
 import type { AnnotationTask } from "../annotation.model";
 import useFetchData from "@/hooks/useFetchData";
 import {
-  deleteAnnotationTaskByIdUsingDelete, loginAnnotationUsingGet,
+  deleteAnnotationTaskByIdUsingDelete,
   queryAnnotationTasksUsingGet,
   syncAnnotationTaskUsingPost,
+  queryAutoAnnotationTasksUsingGet,
+  deleteAutoAnnotationTaskByIdUsingDelete,
 } from "../annotation.api";
 import { mapAnnotationTask } from "../annotation.const";
 import CreateAnnotationTask from "../Create/components/CreateAnnotationTaskDialog";
@@ -21,11 +23,28 @@ import { ColumnType } from "antd/es/table";
 import { TemplateList } from "../Template";
 // Note: DevelopmentInProgress intentionally not used here
 
+const AUTO_STATUS_LABELS: Record<string, string> = {
+  pending: "等待中",
+  running: "处理中",
+  completed: "已完成",
+  failed: "失败",
+  cancelled: "已取消",
+};
+
+const AUTO_MODEL_SIZE_LABELS: Record<string, string> = {
+  n: "YOLOv8n (最快)",
+  s: "YOLOv8s",
+  m: "YOLOv8m",
+  l: "YOLOv8l (推荐)",
+  x: "YOLOv8x (最精确)",
+};
+
 export default function DataAnnotation() {
   // return <DevelopmentInProgress showTime="2025.10.30" />;
   const [activeTab, setActiveTab] = useState("tasks");
   const [viewMode, setViewMode] = useState<"list" | "card">("list");
   const [showCreateDialog, setShowCreateDialog] = useState(false);
+  const [autoTasks, setAutoTasks] = useState<any[]>([]);
 
   const {
     loading,
@@ -41,6 +60,22 @@ export default function DataAnnotation() {
   const [selectedRowKeys, setSelectedRowKeys] = useState<(string | number)[]>([]);
   const [selectedRows, setSelectedRows] = useState<any[]>([]);
 
+  // 拉取自动标注任务（供轮询和创建成功后立即刷新复用）
+  const refreshAutoTasks = async (silent = false) => {
+    try {
+      const response = await queryAutoAnnotationTasksUsingGet();
+      const tasks = (response as any)?.data || response || [];
+      if (Array.isArray(tasks)) {
+        setAutoTasks(tasks);
+      }
+    } catch (error) {
+      console.error("Failed to fetch auto annotation tasks:", error);
+      if (!silent) {
+        message.error("获取自动标注任务失败");
+      }
+    }
+  };
+
   // prefetch config on mount so clicking annotate is fast and we know whether base URL exists
   // useEffect ensures this runs once
   useEffect(() => {
@@ -58,6 +93,16 @@ export default function DataAnnotation() {
     };
   }, []);
 
+  // 自动标注任务轮询（用于在同一表格中展示处理进度）
+  useEffect(() => {
+    refreshAutoTasks();
+    const timer = setInterval(() => refreshAutoTasks(true), 3000);
+
+    return () => {
+      clearInterval(timer);
+    };
+  }, []);
+
   const handleAnnotate = (task: AnnotationTask) => {
     // Open Label Studio project page in a new tab
     (async () => {
@@ -76,7 +121,6 @@ export default function DataAnnotation() {
 
         if (labelingProjId) {
           // only open external Label Studio when we have a configured base url
-          await loginAnnotationUsingGet(labelingProjId)
           if (base) {
             const target = `${base}/projects/${labelingProjId}/data`;
             window.open(target, "_blank");
@@ -126,6 +170,30 @@ export default function DataAnnotation() {
     });
   };
 
+  const handleDeleteAuto = (task: any) => {
+    Modal.confirm({
+      title: `确认删除自动标注任务「${task.name}」吗？`,
+      content: <div>删除任务后，已生成的标注结果不会被删除。</div>,
+      okText: "删除",
+      okType: "danger",
+      cancelText: "取消",
+      onOk: async () => {
+        try {
+          await deleteAutoAnnotationTaskByIdUsingDelete(task.id);
+          message.success("自动标注任务删除成功");
+          // 重新拉取自动标注任务
+          setAutoTasks((prev) => prev.filter((t: any) => t.id !== task.id));
+          // 清理选中
+          setSelectedRowKeys((keys) => keys.filter((k) => k !== task.id));
+          setSelectedRows((rows) => rows.filter((r) => r.id !== task.id));
+        } catch (e) {
+          console.error(e);
+          message.error("删除失败，请稍后重试");
+        }
+      },
+    });
+  };
+
   const handleSync = (task: AnnotationTask, batchSize: number = 50) => {
     Modal.confirm({
       title: `确认同步标注任务「${task.name}」吗？`,
@@ -156,8 +224,13 @@ export default function DataAnnotation() {
 
   const handleBatchSync = (batchSize: number = 50) => {
     if (!selectedRows || selectedRows.length === 0) return;
+    const manualRows = selectedRows.filter((r) => r._kind !== "auto");
+    if (manualRows.length === 0) {
+      message.warning("请选择手动标注任务进行同步");
+      return;
+    }
     Modal.confirm({
-      title: `确认同步所选 ${selectedRows.length} 个标注任务吗？`,
+      title: `确认同步所选 ${manualRows.length} 个标注任务吗？`,
       content: (
         <div>
           <div>标注工程中文件列表将与数据集保持一致，差异项将会被修正。</div>
@@ -169,7 +242,7 @@ export default function DataAnnotation() {
       onOk: async () => {
         try {
           await Promise.all(
-            selectedRows.map((r) => syncAnnotationTaskUsingPost({ id: r.id, batchSize }))
+            manualRows.map((r) => syncAnnotationTaskUsingPost({ id: r.id, batchSize }))
           );
           message.success("批量同步请求已发送");
           fetchData();
@@ -185,6 +258,8 @@ export default function DataAnnotation() {
 
   const handleBatchDelete = () => {
     if (!selectedRows || selectedRows.length === 0) return;
+    const manualRows = selectedRows.filter((r) => r._kind !== "auto");
+    const autoRows = selectedRows.filter((r) => r._kind === "auto");
     Modal.confirm({
       title: `确认删除所选 ${selectedRows.length} 个标注任务吗？`,
       content: (
@@ -199,7 +274,10 @@ export default function DataAnnotation() {
       onOk: async () => {
         try {
           await Promise.all(
-            selectedRows.map((r) => deleteAnnotationTaskByIdUsingDelete(r.id))
+            [
+              ...manualRows.map((r) => deleteAnnotationTaskByIdUsingDelete(r.id)),
+              ...autoRows.map((r) => deleteAutoAnnotationTaskByIdUsingDelete(r.id)),
+            ]
           );
           message.success("批量删除已完成");
           fetchData();
@@ -238,6 +316,38 @@ export default function DataAnnotation() {
       onClick: handleDelete,
     },
   ];
+  // 合并手动标注任务与自动标注任务
+  const mergedTableData = [
+    // 手动标注任务
+    ...tableData.map((task) => ({
+      ...task,
+      _kind: "manual" as const,
+    })),
+    // 自动标注任务
+    ...autoTasks.map((task: any) => {
+      const sourceList = Array.isArray(task.sourceDatasets)
+        ? task.sourceDatasets
+        : task.datasetName
+        ? [task.datasetName]
+        : [];
+      const datasetName = sourceList.length > 0 ? sourceList.join("，") : "-";
+
+      return {
+        id: task.id,
+        name: task.name,
+        datasetName,
+        createdAt: task.createdAt || "-",
+        updatedAt: task.updatedAt || "-",
+        _kind: "auto" as const,
+        autoStatus: task.status,
+        autoProgress: task.progress,
+        autoProcessedImages: task.processedImages,
+        autoTotalImages: task.totalImages,
+        autoDetectedObjects: task.detectedObjects,
+        autoConfig: task.config || {},
+      };
+    }),
+  ];
 
   const columns: ColumnType<any>[] = [
     {
@@ -246,6 +356,13 @@ export default function DataAnnotation() {
       key: "name",
       fixed: "left" as const,
     },
+    {
+      title: "类型",
+      key: "kind",
+      width: 100,
+      render: (_: any, record: any) =>
+        record._kind === "auto" ? "自动标注" : "手动标注",
+    },
     {
       title: "任务ID",
       dataIndex: "id",
@@ -257,6 +374,88 @@ export default function DataAnnotation() {
       key: "datasetName",
       width: 180,
     },
+    {
+      title: "模型",
+      key: "modelSize",
+      width: 160,
+      render: (_: any, record: any) => {
+        if (record._kind !== "auto") return "-";
+        const size = record.autoConfig?.modelSize;
+        return AUTO_MODEL_SIZE_LABELS[size] || size || "-";
+      },
+    },
+    {
+      title: "置信度",
+      key: "confThreshold",
+      width: 120,
+      render: (_: any, record: any) => {
+        if (record._kind !== "auto") return "-";
+        const threshold = record.autoConfig?.confThreshold;
+        if (typeof threshold !== "number") return "-";
+        return `${(threshold * 100).toFixed(0)}%`;
+      },
+    },
+    {
+      title: "目标类别",
+      key: "targetClasses",
+      width: 160,
+      render: (_: any, record: any) => {
+        if (record._kind !== "auto") return "-";
+        const classes: number[] = record.autoConfig?.targetClasses || [];
+        if (!classes.length) return "全部类别";
+        const text = classes.join(", ");
+        return (
+          <Tooltip title={text}>
+            <span>{`${classes.length} 个类别`}</span>
+          </Tooltip>
+        );
+      },
+    },
+    {
+      title: "自动标注状态",
+      key: "autoStatus",
+      width: 130,
+      render: (_: any, record: any) => {
+        if (record._kind !== "auto") return "-";
+        const status = record.autoStatus as string;
+        const label = AUTO_STATUS_LABELS[status] || status || "-";
+        return <Tag>{label}</Tag>;
+      },
+    },
+    {
+      title: "自动标注进度",
+      key: "autoProgress",
+      width: 200,
+      render: (_: any, record: any) => {
+        if (record._kind !== "auto") return "-";
+        const progress = typeof record.autoProgress === "number" ? record.autoProgress : 0;
+        const processed = record.autoProcessedImages ?? 0;
+        const total = record.autoTotalImages ?? 0;
+        return (
+          <div>
+            <Progress percent={progress} size="small" />
+            <div style={{ fontSize: 12, color: "#999" }}>
+              {processed} / {total}
+            </div>
+          </div>
+        );
+      },
+    },
+    {
+      title: "检测对象数",
+      key: "detectedObjects",
+      width: 120,
+      render: (_: any, record: any) => {
+        if (record._kind !== "auto") return "-";
+        const count = record.autoDetectedObjects;
+        if (typeof count !== "number") return "-";
+        try {
+          return count.toLocaleString();
+        } catch {
+          return String(count);
+        }
+      },
+    },
     {
       title: "创建时间",
       dataIndex: "createdAt",
@@ -277,7 +476,8 @@ export default function DataAnnotation() {
       dataIndex: "actions",
       render: (_: any, task: any) => (
         <div className="flex items-center justify-center space-x-1">
-          {operations.map((operation) => (
+          {task._kind === "manual" &&
+            operations.map((operation) => (
               <Button
                 key={operation.key}
                 type="text"
@@ -286,6 +486,14 @@ export default function DataAnnotation() {
                 title={operation.label}
               />
             ))}
+          {task._kind === "auto" && (
+            <Button
+              type="text"
+              icon={<DeleteOutlined style={{ color: "#f5222d" }} />}
+              onClick={() => handleDeleteAuto(task)}
+              title="删除自动标注任务"
+            />
+          )}
         </div>
       ),
     },
@@ -357,7 +565,7 @@ export default function DataAnnotation() {
                       rowKey="id"
                       loading={loading}
                       columns={columns}
-                      dataSource={tableData}
+                      dataSource={mergedTableData}
                       pagination={pagination}
                       rowSelection={{
                         selectedRowKeys,
@@ -381,7 +589,14 @@ export default function DataAnnotation() {
                 <CreateAnnotationTask
                   open={showCreateDialog}
                   onClose={() => setShowCreateDialog(false)}
-                  onRefresh={fetchData}
+                  onRefresh={(mode?: any) => {
+                    // 手动标注创建成功后刷新标注任务列表
+                    fetchData();
+                    // 自动标注创建成功后立即刷新自动标注任务列表
+                    if (mode === "auto") {
+                      refreshAutoTasks(true);
+                    }
+                  }}
                 />
               </div>
             ),

frontend/src/pages/DataAnnotation/annotation.api.ts

@@ -1,4 +1,4 @@
-import { get, post, put, del } from "@/utils/request";
+import { get, post, put, del, download } from "@/utils/request";
 
 // 标注任务管理相关接口
 export function queryAnnotationTasksUsingGet(params?: any) {
@@ -18,10 +18,6 @@ export function deleteAnnotationTaskByIdUsingDelete(mappingId: string) {
   return del(`/api/annotation/project/${mappingId}`);
 }
 
-export function loginAnnotationUsingGet(mappingId: string) {
-  return get("/api/annotation/project/${mappingId}/login");
-}
-
 // 标签配置管理
 export function getTagConfigUsingGet() {
   return get("/api/annotation/tags/config");
@@ -48,3 +44,24 @@ export function deleteAnnotationTemplateByIdUsingDelete(
 ) {
   return del(`/api/annotation/template/${templateId}`);
 }
+
+// 自动标注任务管理
+export function queryAutoAnnotationTasksUsingGet(params?: any) {
+  return get("/api/annotation/auto", params);
+}
+
+export function createAutoAnnotationTaskUsingPost(data: any) {
+  return post("/api/annotation/auto", data);
+}
+
+export function deleteAutoAnnotationTaskByIdUsingDelete(taskId: string) {
+  return del(`/api/annotation/auto/${taskId}`);
+}
+
+export function getAutoAnnotationTaskStatusUsingGet(taskId: string) {
+  return get(`/api/annotation/auto/${taskId}/status`);
+}
+
+export function downloadAutoAnnotationResultUsingGet(taskId: string) {
+  return download(`/api/annotation/auto/${taskId}/download`);
+}

frontend/src/pages/DataManagement/Detail/DatasetDetail.tsx

@@ -71,12 +71,21 @@ export default function DatasetDetail() {
 
   useEffect(() => {
     fetchDataset();
-    filesOperation.fetchFiles();
+    filesOperation.fetchFiles('', 1, 10); // 从根目录开始，第一页
   }, []);
 
-  const handleRefresh = async (showMessage = true) => {
+  const handleRefresh = async (showMessage = true, prefixOverride?: string) => {
     fetchDataset();
-    filesOperation.fetchFiles();
+    // 刷新当前目录，保持在当前页
+    const targetPrefix =
+      prefixOverride !== undefined
+        ? prefixOverride
+        : filesOperation.pagination.prefix;
+    filesOperation.fetchFiles(
+      targetPrefix,
+      filesOperation.pagination.current,
+      filesOperation.pagination.pageSize
+    );
     if (showMessage) message.success({ content: "数据刷新成功" });
   };
 
@@ -92,12 +101,17 @@ export default function DatasetDetail() {
   };
 
   useEffect(() => {
-    const refreshData = () => {
-      handleRefresh(false);
+    const refreshData = (e: Event) => {
+      const custom = e as CustomEvent<{ prefix?: string }>;
+      const prefixOverride = custom.detail?.prefix;
+      handleRefresh(false, prefixOverride);
     };
-    window.addEventListener("update:dataset", refreshData);
+    window.addEventListener("update:dataset", refreshData as EventListener);
     return () => {
-      window.removeEventListener("update:dataset", refreshData);
+      window.removeEventListener(
+        "update:dataset",
+        refreshData as EventListener
+      );
     };
   }, []);
 
@@ -232,6 +246,7 @@ export default function DatasetDetail() {
         data={dataset}
         open={showUploadDialog}
         onClose={() => setShowUploadDialog(false)}
+        prefix={filesOperation.pagination.prefix}
         updateEvent="update:dataset"
       />
       <EditDataset

frontend/src/pages/DataManagement/Detail/components/ImportConfiguration.tsx

@@ -13,17 +13,20 @@ export default function ImportConfiguration({
   open,
   onClose,
   updateEvent = "update:dataset",
+  prefix,
 }: {
   data: Dataset | null;
   open: boolean;
   onClose: () => void;
   updateEvent?: string;
+  prefix?: string;
 }) {
   const [form] = Form.useForm();
   const [collectionOptions, setCollectionOptions] = useState([]);
   const [importConfig, setImportConfig] = useState<any>({
     source: DataSource.UPLOAD,
   });
+  const [currentPrefix, setCurrentPrefix] = useState<string>("");
 
   const [fileList, setFileList] = useState<UploadFile[]>([]);
   const fileSliceList = useMemo(() => {
@@ -45,6 +48,7 @@ export default function ImportConfiguration({
     fileList.forEach((file) => {
       formData.append("file", file);
     });
+    console.log('[ImportConfiguration] Uploading with currentPrefix:', currentPrefix);
     window.dispatchEvent(
       new CustomEvent("upload:dataset", {
         detail: {
@@ -52,6 +56,7 @@ export default function ImportConfiguration({
           files: fileSliceList,
           updateEvent,
           hasArchive: importConfig.hasArchive,
+          prefix: currentPrefix,
         },
       })
     );
@@ -82,14 +87,17 @@ export default function ImportConfiguration({
   };
 
   const resetState = () => {
+    console.log('[ImportConfiguration] resetState called, preserving currentPrefix:', currentPrefix);
     form.resetFields();
     setFileList([]);
     form.setFieldsValue({ files: null });
     setImportConfig({ source: importConfig.source ? importConfig.source : DataSource.UPLOAD });
+    console.log('[ImportConfiguration] resetState done, currentPrefix still:', currentPrefix);
   };
 
   const handleImportData = async () => {
     if (!data) return;
+    console.log('[ImportConfiguration] handleImportData called, currentPrefix:', currentPrefix);
     if (importConfig.source === DataSource.UPLOAD) {
       await handleUpload(data);
     } else if (importConfig.source === DataSource.COLLECTION) {
@@ -102,10 +110,19 @@ export default function ImportConfiguration({
 
   useEffect(() => {
     if (open) {
+      setCurrentPrefix(prefix || "");
+      console.log('[ImportConfiguration] Modal opened with prefix:', prefix);
       resetState();
       fetchCollectionTasks();
     }
-  }, [open, importConfig.source]);
+  }, [open]);
+
+  // Separate effect for fetching collection tasks when source changes
+  useEffect(() => {
+    if (open && importConfig.source === DataSource.COLLECTION) {
+      fetchCollectionTasks();
+    }
+  }, [importConfig.source]);
 
   return (
     <Modal

frontend/src/pages/DataManagement/Detail/components/Overview.tsx

@@ -1,9 +1,10 @@
-import { Button, Descriptions, DescriptionsProps, Modal, Table } from "antd";
+import { App, Button, Descriptions, DescriptionsProps, Modal, Table, Input } from "antd";
 import { formatBytes, formatDateTime } from "@/utils/unit";
 import { Download, Trash2, Folder, File } from "lucide-react";
 import { datasetTypeMap } from "../../dataset.const";
 
 export default function Overview({ dataset, filesOperation, fetchDataset }) {
+  const { modal, message } = App.useApp();
   const {
     fileList,
     pagination,
@@ -17,6 +18,9 @@ export default function Overview({ dataset, filesOperation, fetchDataset }) {
     handleDownloadFile,
     handleBatchDeleteFiles,
     handleBatchExport,
+    handleCreateDirectory,
+    handleDownloadDirectory,
+    handleDeleteDirectory,
   } = filesOperation;
 
   // 文件列表多选配置
@@ -123,8 +127,9 @@ export default function Overview({ dataset, filesOperation, fetchDataset }) {
               type="link"
               onClick={(e) => {
                 const currentPath = filesOperation.pagination.prefix || '';
-                const newPath = `${currentPath}${record.fileName}`;
-                filesOperation.fetchFiles(newPath);
+                // 文件夹路径必须以斜杠结尾
+                const newPath = `${currentPath}${record.fileName}/`;
+                filesOperation.fetchFiles(newPath, 1, filesOperation.pagination.pageSize);
               }}
             >
               {content}
@@ -150,11 +155,24 @@ export default function Overview({ dataset, filesOperation, fetchDataset }) {
       render: (text: number, record: any) => {
         const isDirectory = record.id.startsWith('directory-');
         if (isDirectory) {
-          return "-";
+          return formatBytes(record.fileSize || 0);
         }
         return formatBytes(text)
       },
     },
+    {
+      title: "包含文件数",
+      dataIndex: "fileCount",
+      key: "fileCount",
+      width: 120,
+      render: (text: number, record: any) => {
+        const isDirectory = record.id.startsWith('directory-');
+        if (!isDirectory) {
+          return "-";
+        }
+        return record.fileCount ?? 0;
+      },
+    },
     {
       title: "上传时间",
       dataIndex: "uploadTime",
@@ -169,9 +187,43 @@ export default function Overview({ dataset, filesOperation, fetchDataset }) {
       fixed: "right",
       render: (_, record) => {
         const isDirectory = record.id.startsWith('directory-');
+        
         if (isDirectory) {
-          return <div className="flex"/>;
+          const currentPath = filesOperation.pagination.prefix || '';
+          const fullPath = `${currentPath}${record.fileName}/`;
+          
+          return (
+            <div className="flex">
+              <Button
+                size="small"
+                type="link"
+                onClick={() => handleDownloadDirectory(fullPath, record.fileName)}
+              >
+                下载
+              </Button>
+              <Button
+                size="small"
+                type="link"
+                onClick={() => {
+                  modal.confirm({
+                    title: '确认删除文件夹？',
+                    content: `删除文件夹 "${record.fileName}" 将同时删除其中的所有文件和子文件夹，此操作不可恢复。`,
+                    okText: '删除',
+                    okType: 'danger',
+                    cancelText: '取消',
+                    onOk: async () => {
+                      await handleDeleteDirectory(fullPath, record.fileName);
+                      fetchDataset();
+                    },
+                  });
+                }}
+              >
+                删除
+              </Button>
+            </div>
+          );
         }
+        
         return (
         <div className="flex">
           <Button
@@ -210,7 +262,39 @@ export default function Overview({ dataset, filesOperation, fetchDataset }) {
         />
 
         {/* 文件列表 */}
-        <h2 className="text-base font-semibold mt-8">文件列表</h2>
+        <div className="flex items-center justify-between mt-8 mb-2">
+          <h2 className="text-base font-semibold">文件列表</h2>
+          <Button
+            type="primary"
+            size="small"
+            onClick={() => {
+              let dirName = "";
+              modal.confirm({
+                title: "新建文件夹",
+                content: (
+                  <Input
+                    autoFocus
+                    placeholder="请输入文件夹名称"
+                    onChange={(e) => {
+                      dirName = e.target.value?.trim();
+                    }}
+                  />
+                ),
+                okText: "确定",
+                cancelText: "取消",
+                onOk: async () => {
+                  if (!dirName) {
+                    message.warning("请输入文件夹名称");
+                    return Promise.reject();
+                  }
+                  await handleCreateDirectory(dirName);
+                },
+              });
+            }}
+          >
+            新建文件夹
+          </Button>
+        </div>
         {selectedFiles.length > 0 && (
           <div className="flex items-center gap-2 p-3 bg-blue-50 rounded-lg border border-blue-200">
             <span className="text-sm text-blue-700 font-medium">
@@ -240,10 +324,14 @@ export default function Overview({ dataset, filesOperation, fetchDataset }) {
                 onClick={() => {
                   // 获取上一级目录
                   const currentPath = filesOperation.pagination.prefix || '';
-                  const pathParts = currentPath.split('/').filter(Boolean);
-                  pathParts.pop(); // 移除最后一个目录
+                  // 移除末尾的斜杠，然后按斜杠分割
+                  const trimmedPath = currentPath.replace(/\/$/, '');
+                  const pathParts = trimmedPath.split('/');
+                  // 移除最后一个目录名
+                  pathParts.pop();
+                  // 重新组合路径，如果还有内容则加斜杠，否则为空
                   const parentPath = pathParts.length > 0 ? `${pathParts.join('/')}/` : '';
-                  filesOperation.fetchFiles(parentPath);
+                  filesOperation.fetchFiles(parentPath, 1, filesOperation.pagination.pageSize);
                 }}
                 className="p-0"
               >
@@ -281,12 +369,7 @@ export default function Overview({ dataset, filesOperation, fetchDataset }) {
               ...pagination,
               showTotal: (total) => `共 ${total} 条`,
               onChange: (page, pageSize) => {
-                filesOperation.setPagination(prev => ({
-                    ...prev,
-                    current: page,
-                    pageSize: pageSize
-                }));
-                filesOperation.fetchFiles(pagination.prefix, page, pageSize);
+                filesOperation.fetchFiles(filesOperation.pagination.prefix, page, pageSize);
               }
             }}
           />

frontend/src/pages/DataManagement/Detail/useFilesOperation.ts

@@ -9,6 +9,9 @@ import {
   downloadFileByIdUsingGet,
   exportDatasetUsingPost,
   queryDatasetFilesUsingGet,
+  createDatasetDirectoryUsingPost,
+  downloadDirectoryUsingGet,
+  deleteDirectoryUsingDelete,
 } from "../dataset.api";
 import { useParams } from "react-router";
 
@@ -31,18 +34,16 @@ export function useFilesOperation(dataset: Dataset) {
   const [previewContent, setPreviewContent] = useState("");
   const [previewFileName, setPreviewFileName] = useState("");
 
-  const fetchFiles = async (prefix: string = '', current, pageSize) => {
-    const params: any = {
-      page: current ? current : pagination.current,
-      size: pageSize ? pageSize : pagination.pageSize,
-      isWithDirectory: true,
-    };
+  const fetchFiles = async (prefix?: string, current?, pageSize?) => {
+    // 如果明确传了 prefix（包括空字符串），使用传入的值；否则使用当前 pagination.prefix
+    const targetPrefix = prefix !== undefined ? prefix : (pagination.prefix || '');
     
-    if (prefix !== undefined) {
-      params.prefix = prefix;
-    } else if (pagination.prefix) {
-      params.prefix = pagination.prefix;
-    }
+    const params: any = {
+      page: current !== undefined ? current : pagination.current,
+      size: pageSize !== undefined ? pageSize : pagination.pageSize,
+      isWithDirectory: true,
+      prefix: targetPrefix,
+    };
 
     const { data } = await queryDatasetFilesUsingGet(id!, params);
     setFileList(data.content || []);
@@ -50,7 +51,9 @@ export function useFilesOperation(dataset: Dataset) {
     // Update pagination with current prefix
     setPagination(prev => ({
       ...prev,
-      prefix: prefix !== undefined ? prefix : prev.prefix,
+      current: params.page,
+      pageSize: params.size,
+      prefix: targetPrefix,
       total: data.totalElements || 0,
     }));
   };
@@ -145,5 +148,39 @@ export function useFilesOperation(dataset: Dataset) {
     handleShowFile,
     handleDeleteFile,
     handleBatchExport,
+    handleCreateDirectory: async (directoryName: string) => {
+      const currentPrefix = pagination.prefix || "";
+      try {
+        await createDatasetDirectoryUsingPost(dataset.id, {
+          parentPrefix: currentPrefix,
+          directoryName,
+        });
+        // 创建成功后刷新当前目录，重置到第一页
+        await fetchFiles(currentPrefix, 1, pagination.pageSize);
+        message.success({ content: `文件夹 ${directoryName} 创建成功` });
+      } catch (error) {
+        message.error({ content: `文件夹 ${directoryName} 创建失败` });
+        throw error;
+      }
+    },
+    handleDownloadDirectory: async (directoryPath: string, directoryName: string) => {
+      try {
+        await downloadDirectoryUsingGet(dataset.id, directoryPath);
+        message.success({ content: `文件夹 ${directoryName} 下载成功` });
+      } catch (error) {
+        message.error({ content: `文件夹 ${directoryName} 下载失败` });
+      }
+    },
+    handleDeleteDirectory: async (directoryPath: string, directoryName: string) => {
+      try {
+        await deleteDirectoryUsingDelete(dataset.id, directoryPath);
+        // 删除成功后刷新当前目录
+        const currentPrefix = pagination.prefix || "";
+        await fetchFiles(currentPrefix, 1, pagination.pageSize);
+        message.success({ content: `文件夹 ${directoryName} 已删除` });
+      } catch (error) {
+        message.error({ content: `文件夹 ${directoryName} 删除失败` });
+      }
+    },
   };
 }

frontend/src/pages/DataManagement/Home/DataManagement.tsx

@@ -391,6 +391,7 @@ export default function DatasetManagementPage() {
           setCurrentDataset(null);
           setShowUploadDialog(false);
         }}
+        prefix=""
         updateEvent="update:datasets"
       />
     </div>

frontend/src/pages/DataManagement/dataset.api.ts

@@ -54,6 +54,35 @@ export function uploadDatasetFileUsingPost(id: string | number, data: any) {
   return post(`/api/data-management/datasets/${id}/files`, data);
 }
 
+// 新建数据集文件夹
+export function createDatasetDirectoryUsingPost(
+  id: string | number,
+  data: { parentPrefix?: string; directoryName: string }
+) {
+  return post(`/api/data-management/datasets/${id}/files/directories`, data);
+}
+
+// 下载文件夹（打包为zip）
+export function downloadDirectoryUsingGet(
+  id: string | number,
+  directoryPath: string
+) {
+  const dirName = directoryPath.split('/').filter(Boolean).pop() || 'folder';
+  return download(
+    `/api/data-management/datasets/${id}/files/directories/download?prefix=${encodeURIComponent(directoryPath)}`,
+    null,
+    `${dirName}.zip`
+  );
+}
+
+// 删除文件夹（递归删除）
+export function deleteDirectoryUsingDelete(
+  id: string | number,
+  directoryPath: string
+) {
+  return del(`/api/data-management/datasets/${id}/files/directories?prefix=${encodeURIComponent(directoryPath)}`);
+}
+
 export function downloadFileByIdUsingGet(
   id: string | number,
   fileId: string | number,

frontend/src/pages/Layout/TaskUpload.tsx

@@ -19,8 +19,10 @@ export default function TaskUpload() {
 
   useEffect(() => {
     const uploadHandler = (e: any) => {
+      console.log('[TaskUpload] Received upload event detail:', e.detail);
       const { files } = e.detail;
       const task = createTask(e.detail);
+      console.log('[TaskUpload] Created task with prefix:', task.prefix);
       handleUpload({ task, files });
     };
     window.addEventListener("upload:dataset", uploadHandler);

runtime/datamate-python/app/db/models/annotation_management.py

@@ -1,9 +1,7 @@
-"""
-Tables of Annotation Management Module
-"""
+"""Tables of Annotation Management Module"""
 
 import uuid
-from sqlalchemy import Column, String, BigInteger, Boolean, TIMESTAMP, Text, Integer, JSON, Date, ForeignKey
+from sqlalchemy import Column, String, Boolean, TIMESTAMP, Text, Integer, JSON, ForeignKey
 from sqlalchemy.sql import func
 
 from app.db.session import Base
@@ -58,3 +56,40 @@ class LabelingProject(Base):
     def is_deleted(self) -> bool:
         """检查是否已被软删除"""
         return self.deleted_at is not None
+
+
+class AutoAnnotationTask(Base):
+    """自动标注任务模型，对应表 t_dm_auto_annotation_tasks"""
+
+    __tablename__ = "t_dm_auto_annotation_tasks"
+
+    id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()), comment="UUID")
+    name = Column(String(255), nullable=False, comment="任务名称")
+    dataset_id = Column(String(36), nullable=False, comment="数据集ID")
+    dataset_name = Column(String(255), nullable=True, comment="数据集名称（冗余字段，方便查询）")
+    config = Column(JSON, nullable=False, comment="任务配置（模型规模、置信度等）")
+    file_ids = Column(JSON, nullable=True, comment="要处理的文件ID列表，为空则处理数据集所有图像")
+    status = Column(String(50), nullable=False, default="pending", comment="任务状态: pending/running/completed/failed")
+    progress = Column(Integer, default=0, comment="任务进度 0-100")
+    total_images = Column(Integer, default=0, comment="总图片数")
+    processed_images = Column(Integer, default=0, comment="已处理图片数")
+    detected_objects = Column(Integer, default=0, comment="检测到的对象总数")
+    output_path = Column(String(500), nullable=True, comment="输出路径")
+    error_message = Column(Text, nullable=True, comment="错误信息")
+    created_at = Column(TIMESTAMP, server_default=func.current_timestamp(), comment="创建时间")
+    updated_at = Column(
+        TIMESTAMP,
+        server_default=func.current_timestamp(),
+        onupdate=func.current_timestamp(),
+        comment="更新时间",
+    )
+    completed_at = Column(TIMESTAMP, nullable=True, comment="完成时间")
+    deleted_at = Column(TIMESTAMP, nullable=True, comment="删除时间（软删除）")
+
+    def __repr__(self) -> str:  # pragma: no cover - repr 简单返回
+        return f"<AutoAnnotationTask(id={self.id}, name={self.name}, status={self.status})>"
+
+    @property
+    def is_deleted(self) -> bool:
+        """检查是否已被软删除"""
+        return self.deleted_at is not None

runtime/datamate-python/app/module/annotation/interface/__init__.py

@@ -4,6 +4,7 @@ from .config import router as about_router
 from .project import router as project_router
 from .task import router as task_router
 from .template import router as template_router
+from .auto import router as auto_router
 
 router = APIRouter(
     prefix="/annotation",
@@ -14,3 +15,4 @@ router.include_router(about_router)
 router.include_router(project_router)
 router.include_router(task_router)
 router.include_router(template_router)
+router.include_router(auto_router)

runtime/datamate-python/app/module/annotation/interface/auto.py

@@ -0,0 +1,196 @@
+"""FastAPI routes for Auto Annotation tasks.
+
+These routes back the frontend AutoAnnotation module:
+  - GET  /api/annotation/auto
+  - POST /api/annotation/auto
+  - DELETE /api/annotation/auto/{task_id}
+  - GET  /api/annotation/auto/{task_id}/status (simple wrapper)
+
+"""
+from __future__ import annotations
+
+from typing import List
+
+from fastapi import APIRouter, Depends, HTTPException, Path
+from fastapi.responses import StreamingResponse
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.db.session import get_db
+from app.module.shared.schema import StandardResponse
+from app.module.dataset import DatasetManagementService
+from app.core.logging import get_logger
+
+from ..schema.auto import (
+    CreateAutoAnnotationTaskRequest,
+    AutoAnnotationTaskResponse,
+)
+from ..service.auto import AutoAnnotationTaskService
+
+
+router = APIRouter(
+    prefix="/auto",
+    tags=["annotation/auto"],
+)
+
+logger = get_logger(__name__)
+service = AutoAnnotationTaskService()
+
+
+@router.get("", response_model=StandardResponse[List[AutoAnnotationTaskResponse]])
+async def list_auto_annotation_tasks(
+    db: AsyncSession = Depends(get_db),
+):
+    """获取自动标注任务列表。
+
+    前端当前不传分页参数，这里直接返回所有未删除任务。
+    """
+
+    tasks = await service.list_tasks(db)
+    return StandardResponse(
+        code=200,
+        message="success",
+        data=tasks,
+    )
+
+
+@router.post("", response_model=StandardResponse[AutoAnnotationTaskResponse])
+async def create_auto_annotation_task(
+    request: CreateAutoAnnotationTaskRequest,
+    db: AsyncSession = Depends(get_db),
+):
+    """创建自动标注任务。
+
+    当前仅创建任务记录并置为 pending，实际执行由后续调度/worker 完成。
+    """
+
+    logger.info(
+        "Creating auto annotation task: name=%s, dataset_id=%s, config=%s, file_ids=%s",
+        request.name,
+        request.dataset_id,
+        request.config.model_dump(by_alias=True),
+        request.file_ids,
+    )
+
+    # 尝试获取数据集名称和文件数量用于冗余字段，失败时不阻塞任务创建
+    dataset_name = None
+    total_images = 0
+    try:
+        dm_client = DatasetManagementService(db)
+        # Service.get_dataset 返回 DatasetResponse，包含 name 和 fileCount
+        dataset = await dm_client.get_dataset(request.dataset_id)
+        if dataset is not None:
+            dataset_name = dataset.name
+            # 如果提供了 file_ids，则 total_images 为选中文件数；否则使用数据集文件数
+            if request.file_ids:
+                total_images = len(request.file_ids)
+            else:
+                total_images = getattr(dataset, "fileCount", 0) or 0
+    except Exception as e:  # pragma: no cover - 容错
+        logger.warning("Failed to fetch dataset name for auto task: %s", e)
+
+    task = await service.create_task(
+        db,
+        request,
+        dataset_name=dataset_name,
+        total_images=total_images,
+    )
+
+    return StandardResponse(
+        code=200,
+        message="success",
+        data=task,
+    )
+
+
+@router.get("/{task_id}/status", response_model=StandardResponse[AutoAnnotationTaskResponse])
+async def get_auto_annotation_task_status(
+    task_id: str = Path(..., description="任务ID"),
+    db: AsyncSession = Depends(get_db),
+):
+    """获取单个自动标注任务状态。
+
+    前端当前主要通过列表轮询，这里提供按 ID 查询的补充接口。
+    """
+
+    task = await service.get_task(db, task_id)
+    if not task:
+        raise HTTPException(status_code=404, detail="Task not found")
+
+    return StandardResponse(
+        code=200,
+        message="success",
+        data=task,
+    )
+
+
+@router.delete("/{task_id}", response_model=StandardResponse[bool])
+async def delete_auto_annotation_task(
+    task_id: str = Path(..., description="任务ID"),
+    db: AsyncSession = Depends(get_db),
+):
+    """删除（软删除）自动标注任务，仅标记 deleted_at。"""
+
+    ok = await service.soft_delete_task(db, task_id)
+    if not ok:
+        raise HTTPException(status_code=404, detail="Task not found")
+
+    return StandardResponse(
+        code=200,
+        message="success",
+        data=True,
+    )
+
+
+@router.get("/{task_id}/download")
+async def download_auto_annotation_result(
+    task_id: str = Path(..., description="任务ID"),
+    db: AsyncSession = Depends(get_db),
+):
+    """下载指定自动标注任务的结果 ZIP。"""
+
+    import io
+    import os
+    import zipfile
+    import tempfile
+
+    # 复用服务层获取任务信息
+    task = await service.get_task(db, task_id)
+    if not task:
+        raise HTTPException(status_code=404, detail="Task not found")
+
+    if not task.output_path:
+        raise HTTPException(status_code=400, detail="Task has no output path")
+
+    output_dir = task.output_path
+    if not os.path.isdir(output_dir):
+        raise HTTPException(status_code=404, detail="Output directory not found")
+
+    tmp_fd, tmp_path = tempfile.mkstemp(suffix=".zip")
+    os.close(tmp_fd)
+
+    with zipfile.ZipFile(tmp_path, "w", zipfile.ZIP_DEFLATED) as zf:
+        for root, _, files in os.walk(output_dir):
+            for filename in files:
+                file_path = os.path.join(root, filename)
+                arcname = os.path.relpath(file_path, output_dir)
+                zf.write(file_path, arcname)
+
+    file_size = os.path.getsize(tmp_path)
+    if file_size == 0:
+        raise HTTPException(status_code=500, detail="Generated ZIP is empty")
+
+    def iterfile():
+        with open(tmp_path, "rb") as f:
+            while True:
+                chunk = f.read(8192)
+                if not chunk:
+                    break
+                yield chunk
+
+    filename = f"{task.name}_annotations.zip"
+    headers = {
+        "Content-Disposition": f'attachment; filename="{filename}"',
+        "Content-Length": str(file_size),
+    }
+
+    return StreamingResponse(iterfile(), media_type="application/zip", headers=headers)

runtime/datamate-python/app/module/annotation/schema/auto.py

@@ -0,0 +1,73 @@
+"""Schemas for Auto Annotation tasks"""
+from __future__ import annotations
+
+from typing import List, Optional, Dict, Any
+from datetime import datetime
+
+from pydantic import BaseModel, Field, ConfigDict
+
+
+class AutoAnnotationConfig(BaseModel):
+    """自动标注任务配置（与前端 payload 对齐）"""
+
+    model_size: str = Field(alias="modelSize", description="模型规模: n/s/m/l/x")
+    conf_threshold: float = Field(alias="confThreshold", description="置信度阈值 0-1")
+    target_classes: List[int] = Field(
+        default_factory=list,
+        alias="targetClasses",
+        description="目标类别ID列表，空表示全部类别",
+    )
+    output_dataset_name: Optional[str] = Field(
+        default=None,
+        alias="outputDatasetName",
+        description="自动标注结果要写入的新数据集名称（可选）",
+    )
+
+    model_config = ConfigDict(populate_by_name=True)
+
+
+class CreateAutoAnnotationTaskRequest(BaseModel):
+    """创建自动标注任务的请求体，对齐前端 CreateAutoAnnotationDialog 发送的结构"""
+
+    name: str = Field(..., min_length=1, max_length=255, description="任务名称")
+    dataset_id: str = Field(..., alias="datasetId", description="数据集ID")
+    config: AutoAnnotationConfig = Field(..., description="任务配置")
+    file_ids: Optional[List[str]] = Field(None, alias="fileIds", description="要处理的文件ID列表，为空则处理数据集中所有图像")
+
+    model_config = ConfigDict(populate_by_name=True)
+
+
+class AutoAnnotationTaskResponse(BaseModel):
+    """自动标注任务响应模型（列表/详情均可复用）"""
+
+    id: str = Field(..., description="任务ID")
+    name: str = Field(..., description="任务名称")
+    dataset_id: str = Field(..., alias="datasetId", description="数据集ID")
+    dataset_name: Optional[str] = Field(None, alias="datasetName", description="数据集名称")
+    source_datasets: Optional[List[str]] = Field(
+        default=None,
+        alias="sourceDatasets",
+        description="本任务实际处理涉及到的所有数据集名称列表",
+    )
+    config: Dict[str, Any] = Field(..., description="任务配置")
+    status: str = Field(..., description="任务状态")
+    progress: int = Field(..., description="任务进度 0-100")
+    total_images: int = Field(..., alias="totalImages", description="总图片数")
+    processed_images: int = Field(..., alias="processedImages", description="已处理图片数")
+    detected_objects: int = Field(..., alias="detectedObjects", description="检测到的对象总数")
+    output_path: Optional[str] = Field(None, alias="outputPath", description="输出路径")
+    error_message: Optional[str] = Field(None, alias="errorMessage", description="错误信息")
+    created_at: datetime = Field(..., alias="createdAt", description="创建时间")
+    updated_at: Optional[datetime] = Field(None, alias="updatedAt", description="更新时间")
+    completed_at: Optional[datetime] = Field(None, alias="completedAt", description="完成时间")
+
+    model_config = ConfigDict(populate_by_name=True, from_attributes=True)
+
+
+class AutoAnnotationTaskListResponse(BaseModel):
+    """自动标注任务列表响应，目前前端直接使用数组，这里预留分页结构"""
+
+    content: List[AutoAnnotationTaskResponse] = Field(..., description="任务列表")
+    total: int = Field(..., description="总数")
+
+    model_config = ConfigDict(populate_by_name=True)

runtime/datamate-python/app/module/annotation/service/auto.py

@@ -0,0 +1,154 @@
+"""Service layer for Auto Annotation tasks"""
+from __future__ import annotations
+
+from typing import List, Optional
+from datetime import datetime
+from uuid import uuid4
+
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.db.models.annotation_management import AutoAnnotationTask
+from app.db.models.dataset_management import Dataset, DatasetFiles
+
+from ..schema.auto import (
+    CreateAutoAnnotationTaskRequest,
+    AutoAnnotationTaskResponse,
+)
+
+
+class AutoAnnotationTaskService:
+    """自动标注任务服务（仅管理任务元数据，真正执行由 runtime 负责）"""
+
+    async def create_task(
+        self,
+        db: AsyncSession,
+        request: CreateAutoAnnotationTaskRequest,
+        dataset_name: Optional[str] = None,
+        total_images: int = 0,
+    ) -> AutoAnnotationTaskResponse:
+        """创建自动标注任务，初始状态为 pending。
+
+        这里仅插入任务记录，不负责真正执行 YOLO 推理，
+        后续可以由调度器/worker 读取该表并更新进度。
+        """
+
+        now = datetime.now()
+
+        task = AutoAnnotationTask(
+            id=str(uuid4()),
+            name=request.name,
+            dataset_id=request.dataset_id,
+            dataset_name=dataset_name,
+            config=request.config.model_dump(by_alias=True),
+            file_ids=request.file_ids,  # 存储用户选择的文件ID列表
+            status="pending",
+            progress=0,
+            total_images=total_images,
+            processed_images=0,
+            detected_objects=0,
+            created_at=now,
+            updated_at=now,
+        )
+
+        db.add(task)
+        await db.commit()
+        await db.refresh(task)
+
+        # 创建后附带 sourceDatasets 信息（通常只有一个原始数据集）
+        resp = AutoAnnotationTaskResponse.model_validate(task)
+        try:
+            resp.source_datasets = await self._compute_source_datasets(db, task)
+        except Exception:
+            resp.source_datasets = [dataset_name] if dataset_name else [request.dataset_id]
+        return resp
+
+    async def list_tasks(self, db: AsyncSession) -> List[AutoAnnotationTaskResponse]:
+        """获取未软删除的自动标注任务列表，按创建时间倒序。"""
+
+        result = await db.execute(
+            select(AutoAnnotationTask)
+            .where(AutoAnnotationTask.deleted_at.is_(None))
+            .order_by(AutoAnnotationTask.created_at.desc())
+        )
+        tasks: List[AutoAnnotationTask] = list(result.scalars().all())
+
+        responses: List[AutoAnnotationTaskResponse] = []
+        for task in tasks:
+            resp = AutoAnnotationTaskResponse.model_validate(task)
+            try:
+                resp.source_datasets = await self._compute_source_datasets(db, task)
+            except Exception:
+                # 出错时降级为单个 datasetName/datasetId
+                fallback_name = getattr(task, "dataset_name", None)
+                fallback_id = getattr(task, "dataset_id", "")
+                resp.source_datasets = [fallback_name] if fallback_name else [fallback_id]
+            responses.append(resp)
+
+        return responses
+
+    async def get_task(self, db: AsyncSession, task_id: str) -> Optional[AutoAnnotationTaskResponse]:
+        result = await db.execute(
+            select(AutoAnnotationTask).where(
+                AutoAnnotationTask.id == task_id,
+                AutoAnnotationTask.deleted_at.is_(None),
+            )
+        )
+        task = result.scalar_one_or_none()
+        if not task:
+            return None
+
+        resp = AutoAnnotationTaskResponse.model_validate(task)
+        try:
+            resp.source_datasets = await self._compute_source_datasets(db, task)
+        except Exception:
+            fallback_name = getattr(task, "dataset_name", None)
+            fallback_id = getattr(task, "dataset_id", "")
+            resp.source_datasets = [fallback_name] if fallback_name else [fallback_id]
+        return resp
+
+    async def _compute_source_datasets(
+        self,
+        db: AsyncSession,
+        task: AutoAnnotationTask,
+    ) -> List[str]:
+        """根据任务的 file_ids 推断实际涉及到的所有数据集名称。
+
+        - 如果存在 file_ids，则通过 t_dm_dataset_files 反查 dataset_id，再关联 t_dm_datasets 获取名称；
+        - 如果没有 file_ids，则退回到任务上冗余的 dataset_name/dataset_id。
+        """
+
+        file_ids = task.file_ids or []
+        if file_ids:
+            stmt = (
+                select(Dataset.name)
+                .join(DatasetFiles, Dataset.id == DatasetFiles.dataset_id)
+                .where(DatasetFiles.id.in_(file_ids))
+                .distinct()
+            )
+            result = await db.execute(stmt)
+            names = [row[0] for row in result.fetchall() if row[0]]
+            if names:
+                return names
+
+        # 回退：只显示一个数据集
+        if task.dataset_name:
+            return [task.dataset_name]
+        if task.dataset_id:
+            return [task.dataset_id]
+        return []
+
+    async def soft_delete_task(self, db: AsyncSession, task_id: str) -> bool:
+        result = await db.execute(
+            select(AutoAnnotationTask).where(
+                AutoAnnotationTask.id == task_id,
+                AutoAnnotationTask.deleted_at.is_(None),
+            )
+        )
+        task = result.scalar_one_or_none()
+        if not task:
+            return False
+
+        task.deleted_at = datetime.now()
+        await db.commit()
+        return True

runtime/datamate-python/deploy/docker-entrypoint.sh

@@ -1,8 +1,8 @@
 #!/bin/bash
 set -e
 
-if [-d $LOCAL_FILES_DOCUMENT_ROOT ] && $LOCAL_FILES_SERVING_ENABLED; then
-  echo "Using local document root: $LOCAL_FILES_DOCUMENT_ROOT"
+if [ -d "${LOCAL_FILES_DOCUMENT_ROOT}" ] && [ "${LOCAL_FILES_SERVING_ENABLED}" = "true" ]; then
+  echo "Using local document root: ${LOCAL_FILES_DOCUMENT_ROOT}"
 fi
 
 # 启动应用

runtime/ops/__init__.py

@@ -0,0 +1,17 @@
+# -*- coding: utf-8 -*-
+"""Datamate built-in operators package.
+
+This package contains built-in operators for filtering, slicing, annotation, etc.
+It is mounted into the runtime container under ``datamate.ops`` so that
+``from datamate.ops.annotation...`` imports work correctly.
+"""
+
+__all__ = [
+    "annotation",
+    "filter",
+    "formatter",
+    "llms",
+    "mapper",
+    "slicer",
+    "user",
+]

runtime/ops/annotation/__init__.py

@@ -0,0 +1,6 @@
+# -*- coding: utf-8 -*-
+"""Annotation-related operators (e.g. YOLO detection)."""
+
+__all__ = [
+    "image_object_detection_bounding_box",
+]

runtime/ops/annotation/image_object_detection_bounding_box/__init__.py

@@ -0,0 +1,9 @@
+"""Image object detection (YOLOv8) operator package.
+
+This package exposes the ImageObjectDetectionBoundingBox annotator so that
+the auto-annotation worker can import it via different module paths.
+"""
+
+from .process import ImageObjectDetectionBoundingBox
+
+__all__ = ["ImageObjectDetectionBoundingBox"]

runtime/ops/annotation/image_object_detection_bounding_box/metadata.yml

@@ -0,0 +1,3 @@
+name: image_object_detection_bounding_box
+version: 0.1.0
+description: "YOLOv8-based object detection operator for auto annotation"

runtime/ops/annotation/image_object_detection_bounding_box/process.py

@@ -0,0 +1,214 @@
+#!/user/bin/python
+# -- encoding: utf-8 --
+
+"""
+Description: 图像目标检测算子
+Create: 2025/12/17
+"""
+import os
+import json
+import time
+from typing import Dict, Any
+import cv2
+import numpy as np
+from loguru import logger
+
+try:
+    from ultralytics import YOLO
+except ImportError:
+    logger.warning("ultralytics not installed. Please install it using: pip install ultralytics")
+    YOLO = None
+
+from datamate.core.base_op import Mapper
+
+
+# COCO 80 类别映射
+COCO_CLASS_MAP = {
+    0: "person", 1: "bicycle", 2: "car", 3: "motorcycle", 4: "airplane",
+    5: "bus", 6: "train", 7: "truck", 8: "boat", 9: "traffic light",
+    10: "fire hydrant", 11: "stop sign", 12: "parking meter", 13: "bench",
+    14: "bird", 15: "cat", 16: "dog", 17: "horse", 18: "sheep", 19: "cow",
+    20: "elephant", 21: "bear", 22: "zebra", 23: "giraffe", 24: "backpack",
+    25: "umbrella", 26: "handbag", 27: "tie", 28: "suitcase", 29: "frisbee",
+    30: "skis", 31: "snowboard", 32: "sports ball", 33: "kite",
+    34: "baseball bat", 35: "baseball glove", 36: "skateboard",
+    37: "surfboard", 38: "tennis racket", 39: "bottle",
+    40: "wine glass", 41: "cup", 42: "fork", 43: "knife", 44: "spoon",
+    45: "bowl", 46: "banana", 47: "apple", 48: "sandwich", 49: "orange",
+    50: "broccoli", 51: "carrot", 52: "hot dog", 53: "pizza",
+    54: "donut", 55: "cake", 56: "chair", 57: "couch",
+    58: "potted plant", 59: "bed", 60: "dining table", 61: "toilet",
+    62: "tv", 63: "laptop", 64: "mouse", 65: "remote",
+    66: "keyboard", 67: "cell phone", 68: "microwave", 69: "oven",
+    70: "toaster", 71: "sink", 72: "refrigerator", 73: "book",
+    74: "clock", 75: "vase", 76: "scissors", 77: "teddy bear",
+    78: "hair drier", 79: "toothbrush"
+}
+
+
+class ImageObjectDetectionBoundingBox(Mapper):
+    """图像目标检测算子"""
+
+    # 模型映射
+    MODEL_MAP = {
+        "n": "yolov8n.pt",
+        "s": "yolov8s.pt",
+        "m": "yolov8m.pt",
+        "l": "yolov8l.pt",
+        "x": "yolov8x.pt",
+    }
+
+    def __init__(self, *args, **kwargs):
+        super(ImageObjectDetectionBoundingBox, self).__init__(*args, **kwargs)
+        
+        # 获取参数
+        self._model_size = kwargs.get("modelSize", "l")
+        self._conf_threshold = kwargs.get("confThreshold", 0.7)
+        self._target_classes = kwargs.get("targetClasses", [])
+        self._output_dir = kwargs.get("outputDir", None)  # 输出目录
+        
+        # 如果目标类别为空列表，则检测所有类别
+        if not self._target_classes:
+            self._target_classes = None
+        else:
+            # 确保是整数列表
+            self._target_classes = [int(cls_id) for cls_id in self._target_classes]
+        
+        # 获取模型路径
+        model_filename = self.MODEL_MAP.get(self._model_size, "yolov8l.pt")
+        current_dir = os.path.dirname(os.path.abspath(__file__))
+        model_path = os.path.join(current_dir, model_filename)
+        
+        # 初始化模型
+        if YOLO is None:
+            raise ImportError("ultralytics is not installed. Please install it.")
+        
+        if not os.path.exists(model_path):
+            logger.warning(f"Model file {model_path} not found. Downloading from ultralytics...")
+            self.model = YOLO(model_filename)  # 自动下载
+        else:
+            self.model = YOLO(model_path)
+        
+        logger.info(f"Loaded YOLOv8 model: {model_filename}, "
+                   f"conf_threshold: {self._conf_threshold}, "
+                   f"target_classes: {self._target_classes}")
+
+    @staticmethod
+    def _get_color_by_class_id(class_id: int):
+        """根据 class_id 生成稳定颜色（BGR，OpenCV 用）"""
+        np.random.seed(class_id)
+        color = np.random.randint(0, 255, size=3).tolist()
+        return tuple(color)
+
+    def execute(self, sample: Dict[str, Any]) -> Dict[str, Any]:
+        """执行目标检测"""
+        start = time.time()
+        
+        # 读取图像文件
+        image_path = sample.get(self.image_key)
+        if not image_path or not os.path.exists(image_path):
+            logger.warning(f"Image file not found: {image_path}")
+            return sample
+        
+        # 读取图像
+        img = cv2.imread(image_path)
+        if img is None:
+            logger.warning(f"Failed to read image: {image_path}")
+            return sample
+        
+        # 执行目标检测
+        results = self.model(img, conf=self._conf_threshold)
+        r = results[0]
+        
+        # 准备标注数据
+        h, w = img.shape[:2]
+        annotations = {
+            "image": os.path.basename(image_path),
+            "width": w,
+            "height": h,
+            "model_size": self._model_size,
+            "conf_threshold": self._conf_threshold,
+            "selected_class_ids": self._target_classes,
+            "detections": []
+        }
+        
+        # 处理检测结果
+        if r.boxes is not None:
+            for box in r.boxes:
+                cls_id = int(box.cls[0])
+                
+                # 过滤目标类别
+                if self._target_classes is not None and cls_id not in self._target_classes:
+                    continue
+                
+                conf = float(box.conf[0])
+                x1, y1, x2, y2 = map(float, box.xyxy[0])
+                label = COCO_CLASS_MAP.get(cls_id, f"class_{cls_id}")
+                
+                # 记录检测结果
+                annotations["detections"].append({
+                    "label": label,
+                    "class_id": cls_id,
+                    "confidence": round(conf, 4),
+                    "bbox_xyxy": [x1, y1, x2, y2],
+                    "bbox_xywh": [x1, y1, x2 - x1, y2 - y1]
+                })
+                
+                # 在图像上绘制
+                color = self._get_color_by_class_id(cls_id)
+                cv2.rectangle(
+                    img,
+                    (int(x1), int(y1)),
+                    (int(x2), int(y2)),
+                    color,
+                    2
+                )
+                
+                cv2.putText(
+                    img,
+                    f"{label} {conf:.2f}",
+                    (int(x1), max(int(y1) - 5, 10)),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.5,
+                    color,
+                    1
+                )
+        
+        # 确定输出目录
+        if self._output_dir and os.path.exists(self._output_dir):
+            output_dir = self._output_dir
+        else:
+            output_dir = os.path.dirname(image_path)
+        
+        # 创建输出子目录（可选，用于组织文件）
+        images_dir = os.path.join(output_dir, "images")
+        annotations_dir = os.path.join(output_dir, "annotations")
+        os.makedirs(images_dir, exist_ok=True)
+        os.makedirs(annotations_dir, exist_ok=True)
+        
+        # 保持原始文件名（不添加后缀），确保一一对应
+        base_name = os.path.basename(image_path)
+        name_without_ext = os.path.splitext(base_name)[0]
+        
+        # 保存标注图像（保持原始扩展名或使用jpg）
+        output_filename = base_name
+        output_path = os.path.join(images_dir, output_filename)
+        cv2.imwrite(output_path, img)
+        
+        # 保存标注 JSON（文件名与图像对应）
+        json_filename = f"{name_without_ext}.json"
+        json_path = os.path.join(annotations_dir, json_filename)
+        with open(json_path, "w", encoding="utf-8") as f:
+            json.dump(annotations, f, indent=2, ensure_ascii=False)
+        
+        # 更新样本数据
+        sample["detection_count"] = len(annotations["detections"])
+        sample["output_image"] = output_path
+        sample["annotations_file"] = json_path
+        sample["annotations"] = annotations
+        
+        logger.info(f"Image: {os.path.basename(image_path)}, "
+                   f"Detections: {len(annotations['detections'])}, "
+                   f"Time: {(time.time() - start):.4f}s")
+        
+        return sample

runtime/ops/annotation/image_semantic_segmentation/process.py

@@ -0,0 +1,166 @@
+import os
+import json
+from pathlib import Path
+from ultralytics import YOLO
+import cv2
+import numpy as np
+
+
+def get_color_by_class_id(class_id: int):
+    """根据 class_id 生成稳定颜色（BGR）"""
+    np.random.seed(class_id)
+    color = np.random.randint(0, 255, size=3).tolist()
+    return tuple(color)
+
+
+def mask_to_polygons(mask: np.ndarray):
+    """将二值 mask 转换为 COCO 风格多边形列表"""
+    contours, _ = cv2.findContours(
+        mask,
+        cv2.RETR_EXTERNAL,
+        cv2.CHAIN_APPROX_SIMPLE
+    )
+
+    polygons = []
+    for contour in contours:
+        if contour.shape[0] < 3:
+            continue
+        polygon = contour.flatten().tolist()
+        polygons.append(polygon)
+
+    return polygons
+
+
+IMAGE_DIR = "C:/Users/meta/Desktop/Datamate/yolo/Photos"
+OUT_IMG_DIR = "outputs_seg/images"
+OUT_JSON_DIR = "outputs_seg/annotations"
+
+MODEL_MAP = {
+    "n": "yolov8n-seg.pt",
+    "s": "yolov8s-seg.pt",
+    "m": "yolov8m-seg.pt",
+    "l": "yolov8l-seg.pt",
+    "x": "yolov8x-seg.pt",
+}
+MODEL_KEY = "x"
+MODEL_PATH = MODEL_MAP[MODEL_KEY]
+
+CONF_THRES = 0.7
+DRAW_BBOX = True
+
+COCO_CLASS_MAP = {
+    0: "person", 1: "bicycle", 2: "car", 3: "motorcycle", 4: "airplane",
+    5: "bus", 6: "train", 7: "truck", 8: "boat", 9: "traffic light",
+    10: "fire hydrant", 11: "stop sign", 12: "parking meter", 13: "bench",
+    14: "bird", 15: "cat", 16: "dog", 17: "horse", 18: "sheep", 19: "cow",
+    20: "elephant", 21: "bear", 22: "zebra", 23: "giraffe", 24: "backpack",
+    25: "umbrella", 26: "handbag", 27: "tie", 28: "suitcase", 29: "frisbee",
+    30: "skis", 31: "snowboard", 32: "sports ball", 33: "kite",
+    34: "baseball bat", 35: "baseball glove", 36: "skateboard",
+    37: "surfboard", 38: "tennis racket", 39: "bottle",
+    40: "wine glass", 41: "cup", 42: "fork", 43: "knife", 44: "spoon",
+    45: "bowl", 46: "banana", 47: "apple", 48: "sandwich", 49: "orange",
+    50: "broccoli", 51: "carrot", 52: "hot dog", 53: "pizza",
+    54: "donut", 55: "cake", 56: "chair", 57: "couch",
+    58: "potted plant", 59: "bed", 60: "dining table", 61: "toilet",
+    62: "tv", 63: "laptop", 64: "mouse", 65: "remote",
+    66: "keyboard", 67: "cell phone", 68: "microwave", 69: "oven",
+    70: "toaster", 71: "sink", 72: "refrigerator", 73: "book",
+    74: "clock", 75: "vase", 76: "scissors", 77: "teddy bear",
+    78: "hair drier", 79: "toothbrush"
+}
+
+TARGET_CLASS_IDS = [0, 2, 5]
+
+os.makedirs(OUT_IMG_DIR, exist_ok=True)
+os.makedirs(OUT_JSON_DIR, exist_ok=True)
+
+if TARGET_CLASS_IDS is not None:
+    for cid in TARGET_CLASS_IDS:
+        if cid not in COCO_CLASS_MAP:
+            raise ValueError(f"Invalid class id: {cid}")
+
+model = YOLO(MODEL_PATH)
+
+image_paths = list(Path(IMAGE_DIR).glob("*.*"))
+
+for img_path in image_paths:
+    img = cv2.imread(str(img_path))
+    if img is None:
+        print(f"[WARN] Failed to read {img_path}")
+        continue
+
+    results = model(img, conf=CONF_THRES)
+    r = results[0]
+
+    h, w = img.shape[:2]
+    annotations = {
+        "image": img_path.name,
+        "width": w,
+        "height": h,
+        "model_key": MODEL_KEY,
+        "conf_threshold": CONF_THRES,
+        "supported_classes": COCO_CLASS_MAP,
+        "selected_class_ids": TARGET_CLASS_IDS,
+        "instances": []
+    }
+
+    if r.boxes is not None and r.masks is not None:
+        for i, box in enumerate(r.boxes):
+            cls_id = int(box.cls[0])
+            if TARGET_CLASS_IDS is not None and cls_id not in TARGET_CLASS_IDS:
+                continue
+
+            conf = float(box.conf[0])
+            x1, y1, x2, y2 = map(float, box.xyxy[0])
+            label = COCO_CLASS_MAP[cls_id]
+
+            mask = r.masks.data[i].cpu().numpy()
+            mask = (mask > 0.5).astype(np.uint8)
+            mask = cv2.resize(mask, (w, h), interpolation=cv2.INTER_NEAREST)
+
+            color = get_color_by_class_id(cls_id)
+            img[mask == 1] = (
+                img[mask == 1] * 0.5 + np.array(color) * 0.5
+            ).astype(np.uint8)
+
+            if True:
+                cv2.rectangle(
+                    img,
+                    (int(x1), int(y1)),
+                    (int(x2), int(y2)),
+                    color,
+                    2
+                )
+
+                cv2.putText(
+                    img,
+                    f"{label} {conf:.2f}",
+                    (int(x1), max(int(y1) - 5, 10)),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.5,
+                    color,
+                    1
+                )
+
+            polygons = mask_to_polygons(mask)
+
+            annotations["instances"].append({
+                "label": label,
+                "class_id": cls_id,
+                "confidence": round(conf, 4),
+                "bbox_xyxy": [x1, y1, x2, y2],
+                "segmentation": polygons
+            })
+
+    out_img_path = os.path.join(OUT_IMG_DIR, img_path.name)
+    out_json_path = os.path.join(OUT_JSON_DIR, img_path.stem + ".json")
+
+    cv2.imwrite(out_img_path, img)
+
+    with open(out_json_path, "w", encoding="utf-8") as f:
+        json.dump(annotations, f, indent=2, ensure_ascii=False)
+
+    print(f"[OK] {img_path.name}")
+
+print("Segmentation batch finished.")

runtime/ops/pyproject.toml

@@ -31,4 +31,5 @@ dependencies = [
     "sqlalchemy>=2.0.44",
     "xmltodict>=1.0.2",
     "zhconv>=1.4.3",
+    "ultralytics>=8.0.0",
 ]

runtime/python-executor/datamate/auto_annotation_worker.py

@@ -0,0 +1,603 @@
+# -*- coding: utf-8 -*-
+"""Simple background worker for auto-annotation tasks.
+
+This module runs inside the datamate-runtime container (operator_runtime service).
+It polls `t_dm_auto_annotation_tasks` for pending tasks and performs YOLO
+inference using the ImageObjectDetectionBoundingBox operator, updating
+progress back to the same table so that the datamate-python backend and
+frontend can display real-time status.
+
+设计目标（最小可用版本）:
+- 单实例 worker，串行处理 `pending` 状态的任务。
+- 对指定数据集下的所有已完成文件逐张执行目标检测。
+- 按已处理图片数更新 `processed_images`、`progress`、`detected_objects`、`status` 等字段。
+- 失败时将任务标记为 `failed` 并记录 `error_message`。
+
+注意:
+- 为了保持简单，目前不处理 "running" 状态的恢复逻辑；容器重启时，
+  已处于 running 的任务不会被重新拉起，需要后续扩展。
+"""
+from __future__ import annotations
+
+import json
+import os
+import sys
+import threading
+import time
+import uuid
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+from loguru import logger
+from sqlalchemy import text
+
+from datamate.sql_manager.sql_manager import SQLManager
+
+# 尝试多种导入路径，适配不同的打包/安装方式
+ImageObjectDetectionBoundingBox = None  # type: ignore
+try:
+    # 优先使用 datamate.ops 路径（源码 COPY 到 /opt/runtime/datamate/ops 情况）
+    from datamate.ops.annotation.image_object_detection_bounding_box.process import (  # type: ignore
+        ImageObjectDetectionBoundingBox,
+    )
+    logger.info(
+        "Imported ImageObjectDetectionBoundingBox from datamate.ops.annotation.image_object_detection_bounding_box",
+    )
+except Exception as e1:  # pragma: no cover - 导入失败时仅记录日志，避免整体崩溃
+    logger.error(
+        "Failed to import ImageObjectDetectionBoundingBox via datamate.ops: {}",
+        e1,
+    )
+    try:
+        # 兼容顶层 ops 包安装的情况（通过 ops.pth 暴露）
+        from ops.annotation.image_object_detection_bounding_box.process import (  # type: ignore
+            ImageObjectDetectionBoundingBox,
+        )
+        logger.info(
+            "Imported ImageObjectDetectionBoundingBox from top-level ops.annotation.image_object_detection_bounding_box",
+        )
+    except Exception as e2:
+        logger.error(
+            "Failed to import ImageObjectDetectionBoundingBox via top-level ops package: {}",
+            e2,
+        )
+        ImageObjectDetectionBoundingBox = None
+
+
+# 进一步兜底：直接从本地 runtime/ops 目录加载算子（开发环境常用场景）
+if ImageObjectDetectionBoundingBox is None:
+    try:
+        project_root = Path(__file__).resolve().parents[2]
+        ops_root = project_root / "ops"
+        if ops_root.is_dir():
+            # 确保 ops 的父目录在 sys.path 中，这样可以按 "ops.xxx" 导入
+            if str(project_root) not in sys.path:
+                sys.path.insert(0, str(project_root))
+
+            from ops.annotation.image_object_detection_bounding_box.process import (  # type: ignore
+                ImageObjectDetectionBoundingBox,
+            )
+
+            logger.info(
+                "Imported ImageObjectDetectionBoundingBox from local runtime/ops.annotation.image_object_detection_bounding_box",
+            )
+        else:
+            logger.warning(
+                "Local runtime/ops directory not found when trying to import ImageObjectDetectionBoundingBox: {}",
+                ops_root,
+            )
+    except Exception as e3:  # pragma: no cover - 兜底失败仅记录日志
+        logger.error(
+            "Failed to import ImageObjectDetectionBoundingBox from local runtime/ops: {}",
+            e3,
+        )
+        ImageObjectDetectionBoundingBox = None
+
+
+POLL_INTERVAL_SECONDS = float(os.getenv("AUTO_ANNOTATION_POLL_INTERVAL", "5"))
+
+DEFAULT_OUTPUT_ROOT = os.getenv(
+    "AUTO_ANNOTATION_OUTPUT_ROOT", "/dataset"
+)
+
+
+def _fetch_pending_task() -> Optional[Dict[str, Any]]:
+    """从 t_dm_auto_annotation_tasks 中取出一个 pending 任务。"""
+
+    sql = text(
+        """
+        SELECT id, name, dataset_id, dataset_name, config, file_ids, status,
+               total_images, processed_images, detected_objects, output_path
+        FROM t_dm_auto_annotation_tasks
+        WHERE status = 'pending' AND deleted_at IS NULL
+        ORDER BY created_at ASC
+        LIMIT 1
+        """
+    )
+
+    with SQLManager.create_connect() as conn:
+        result = conn.execute(sql).fetchone()
+        if not result:
+            return None
+        row = dict(result._mapping)  # type: ignore[attr-defined]
+
+    try:
+        row["config"] = json.loads(row["config"]) if row.get("config") else {}
+    except Exception:
+        row["config"] = {}
+
+    try:
+        raw_ids = row.get("file_ids")
+        if not raw_ids:
+            row["file_ids"] = None
+        elif isinstance(raw_ids, str):
+            row["file_ids"] = json.loads(raw_ids)
+        else:
+            row["file_ids"] = raw_ids
+    except Exception:
+        row["file_ids"] = None
+    return row
+
+
+def _update_task_status(
+    task_id: str,
+    *,
+    status: str,
+    progress: Optional[int] = None,
+    processed_images: Optional[int] = None,
+    detected_objects: Optional[int] = None,
+    total_images: Optional[int] = None,
+    output_path: Optional[str] = None,
+    error_message: Optional[str] = None,
+    completed: bool = False,
+) -> None:
+    """更新任务的状态和统计字段。"""
+
+    fields: List[str] = ["status = :status", "updated_at = :updated_at"]
+    params: Dict[str, Any] = {
+        "task_id": task_id,
+        "status": status,
+        "updated_at": datetime.now(),
+    }
+
+    if progress is not None:
+        fields.append("progress = :progress")
+        params["progress"] = int(progress)
+    if processed_images is not None:
+        fields.append("processed_images = :processed_images")
+        params["processed_images"] = int(processed_images)
+    if detected_objects is not None:
+        fields.append("detected_objects = :detected_objects")
+        params["detected_objects"] = int(detected_objects)
+    if total_images is not None:
+        fields.append("total_images = :total_images")
+        params["total_images"] = int(total_images)
+    if output_path is not None:
+        fields.append("output_path = :output_path")
+        params["output_path"] = output_path
+    if error_message is not None:
+        fields.append("error_message = :error_message")
+        params["error_message"] = error_message[:2000]
+    if completed:
+        fields.append("completed_at = :completed_at")
+        params["completed_at"] = datetime.now()
+
+    sql = text(
+        f"""
+        UPDATE t_dm_auto_annotation_tasks
+        SET {', '.join(fields)}
+        WHERE id = :task_id
+        """
+    )
+
+    with SQLManager.create_connect() as conn:
+        conn.execute(sql, params)
+
+
+def _load_dataset_files(dataset_id: str) -> List[Tuple[str, str, str]]:
+    """加载指定数据集下的所有已完成文件。"""
+
+    sql = text(
+        """
+        SELECT id, file_path, file_name
+        FROM t_dm_dataset_files
+        WHERE dataset_id = :dataset_id
+          AND status = 'ACTIVE'
+        ORDER BY created_at ASC
+        """
+    )
+
+    with SQLManager.create_connect() as conn:
+        rows = conn.execute(sql, {"dataset_id": dataset_id}).fetchall()
+        return [(str(r[0]), str(r[1]), str(r[2])) for r in rows]
+
+
+def _load_files_by_ids(file_ids: List[str]) -> List[Tuple[str, str, str]]:
+    """根据文件ID列表加载文件记录，支持跨多个数据集。"""
+
+    if not file_ids:
+        return []
+
+    placeholders = ", ".join(f":id{i}" for i in range(len(file_ids)))
+    sql = text(
+        f"""
+        SELECT id, file_path, file_name
+        FROM t_dm_dataset_files
+        WHERE id IN ({placeholders})
+          AND status = 'ACTIVE'
+        ORDER BY created_at ASC
+        """
+    )
+    params = {f"id{i}": str(fid) for i, fid in enumerate(file_ids)}
+
+    with SQLManager.create_connect() as conn:
+        rows = conn.execute(sql, params).fetchall()
+        return [(str(r[0]), str(r[1]), str(r[2])) for r in rows]
+
+
+def _ensure_output_dir(output_dir: str) -> str:
+    """确保输出目录及其 images/、annotations/ 子目录存在。"""
+
+    os.makedirs(output_dir, exist_ok=True)
+    os.makedirs(os.path.join(output_dir, "images"), exist_ok=True)
+    os.makedirs(os.path.join(output_dir, "annotations"), exist_ok=True)
+    return output_dir
+
+
+def _create_output_dataset(
+    source_dataset_id: str,
+    source_dataset_name: str,
+    output_dataset_name: str,
+) -> Tuple[str, str]:
+    """为自动标注结果创建一个新的数据集并返回 (dataset_id, path)。"""
+
+    new_dataset_id = str(uuid.uuid4())
+    dataset_base_path = DEFAULT_OUTPUT_ROOT.rstrip("/") or "/dataset"
+    output_dir = os.path.join(dataset_base_path, new_dataset_id)
+
+    description = (
+        f"Auto annotations for dataset {source_dataset_name or source_dataset_id}"[:255]
+    )
+
+    sql = text(
+        """
+        INSERT INTO t_dm_datasets (id, name, description, dataset_type, path, status)
+        VALUES (:id, :name, :description, :dataset_type, :path, :status)
+        """
+    )
+    params = {
+        "id": new_dataset_id,
+        "name": output_dataset_name,
+        "description": description,
+        "dataset_type": "IMAGE",
+        "path": output_dir,
+        "status": "ACTIVE",
+    }
+
+    with SQLManager.create_connect() as conn:
+        conn.execute(sql, params)
+
+    return new_dataset_id, output_dir
+
+
+def _register_output_dataset(
+    task_id: str,
+    output_dataset_id: str,
+    output_dir: str,
+    output_dataset_name: str,
+    total_images: int,
+) -> None:
+    """将自动标注结果注册到新建的数据集。"""
+
+    images_dir = os.path.join(output_dir, "images")
+    if not os.path.isdir(images_dir):
+        logger.warning(
+            "Auto-annotation images directory not found for task {}: {}",
+            task_id,
+            images_dir,
+        )
+        return
+
+    image_files: List[Tuple[str, str, int]] = []
+    annotation_files: List[Tuple[str, str, int]] = []
+    total_size = 0
+
+    for file_name in sorted(os.listdir(images_dir)):
+        file_path = os.path.join(images_dir, file_name)
+        if not os.path.isfile(file_path):
+            continue
+        try:
+            file_size = os.path.getsize(file_path)
+        except OSError:
+            file_size = 0
+        image_files.append((file_name, file_path, int(file_size)))
+        total_size += int(file_size)
+
+    annotations_dir = os.path.join(output_dir, "annotations")
+    if os.path.isdir(annotations_dir):
+        for file_name in sorted(os.listdir(annotations_dir)):
+            file_path = os.path.join(annotations_dir, file_name)
+            if not os.path.isfile(file_path):
+                continue
+            try:
+                file_size = os.path.getsize(file_path)
+            except OSError:
+                file_size = 0
+            annotation_files.append((file_name, file_path, int(file_size)))
+            total_size += int(file_size)
+
+    if not image_files:
+        logger.warning(
+            "No image files found in auto-annotation output for task {}: {}",
+            task_id,
+            images_dir,
+        )
+        return
+
+    insert_file_sql = text(
+        """
+        INSERT INTO t_dm_dataset_files (
+            id, dataset_id, file_name, file_path, file_type, file_size, status
+        ) VALUES (
+            :id, :dataset_id, :file_name, :file_path, :file_type, :file_size, :status
+        )
+        """
+    )
+    update_dataset_stat_sql = text(
+        """
+        UPDATE t_dm_datasets
+        SET file_count = COALESCE(file_count, 0) + :add_count,
+            size_bytes = COALESCE(size_bytes, 0) + :add_size
+        WHERE id = :dataset_id
+        """
+    )
+
+    with SQLManager.create_connect() as conn:
+        added_count = 0
+
+        for file_name, file_path, file_size in image_files:
+            ext = os.path.splitext(file_name)[1].lstrip(".").upper() or None
+            conn.execute(
+                insert_file_sql,
+                {
+                    "id": str(uuid.uuid4()),
+                    "dataset_id": output_dataset_id,
+                    "file_name": file_name,
+                    "file_path": file_path,
+                    "file_type": ext,
+                    "file_size": int(file_size),
+                    "status": "ACTIVE",
+                },
+            )
+            added_count += 1
+
+        for file_name, file_path, file_size in annotation_files:
+            ext = os.path.splitext(file_name)[1].lstrip(".").upper() or None
+            conn.execute(
+                insert_file_sql,
+                {
+                    "id": str(uuid.uuid4()),
+                    "dataset_id": output_dataset_id,
+                    "file_name": file_name,
+                    "file_path": file_path,
+                    "file_type": ext,
+                    "file_size": int(file_size),
+                    "status": "ACTIVE",
+                },
+            )
+            added_count += 1
+
+        if added_count > 0:
+            conn.execute(
+                update_dataset_stat_sql,
+                {
+                    "dataset_id": output_dataset_id,
+                    "add_count": added_count,
+                    "add_size": int(total_size),
+                },
+            )
+
+    logger.info(
+        "Registered auto-annotation output into dataset: dataset_id={}, name={}, added_files={}, added_size_bytes={}, task_id={}, output_dir={}",
+        output_dataset_id,
+        output_dataset_name,
+        len(image_files) + len(annotation_files),
+        total_size,
+        task_id,
+        output_dir,
+    )
+
+
+def _process_single_task(task: Dict[str, Any]) -> None:
+    """执行单个自动标注任务。"""
+
+    if ImageObjectDetectionBoundingBox is None:
+        logger.error(
+            "YOLO operator not available (import failed earlier), skip auto-annotation task: {}",
+            task["id"],
+        )
+        _update_task_status(
+            task["id"],
+            status="failed",
+            error_message="YOLO operator not available in runtime container",
+        )
+        return
+
+    task_id = str(task["id"])
+    dataset_id = str(task["dataset_id"])
+    task_name = str(task.get("name") or "")
+    source_dataset_name = str(task.get("dataset_name") or "")
+    cfg: Dict[str, Any] = task.get("config") or {}
+    selected_file_ids: Optional[List[str]] = task.get("file_ids") or None
+
+    model_size = cfg.get("modelSize", "l")
+    conf_threshold = float(cfg.get("confThreshold", 0.7))
+    target_classes = cfg.get("targetClasses", []) or []
+    output_dataset_name = cfg.get("outputDatasetName")
+
+    if not output_dataset_name:
+        base_name = source_dataset_name or task_name or f"dataset-{dataset_id[:8]}"
+        output_dataset_name = f"{base_name}_auto_{task_id[:8]}"
+
+    logger.info(
+        "Start processing auto-annotation task: id={}, dataset_id={}, model_size={}, conf_threshold={}, target_classes={}, output_dataset_name={}",
+        task_id,
+        dataset_id,
+        model_size,
+        conf_threshold,
+        target_classes,
+        output_dataset_name,
+    )
+
+    _update_task_status(task_id, status="running", progress=0)
+
+    if selected_file_ids:
+        all_files = _load_files_by_ids(selected_file_ids)
+    else:
+        all_files = _load_dataset_files(dataset_id)
+
+    files = [(path, name) for _, path, name in all_files]
+
+    total_images = len(files)
+    if total_images == 0:
+        logger.warning("No files found for dataset {} when running auto-annotation task {}", dataset_id, task_id)
+        _update_task_status(
+            task_id,
+            status="completed",
+            progress=100,
+            total_images=0,
+            processed_images=0,
+            detected_objects=0,
+            completed=True,
+            output_path=None,
+        )
+        return
+
+    output_dataset_id, output_dir = _create_output_dataset(
+        source_dataset_id=dataset_id,
+        source_dataset_name=source_dataset_name,
+        output_dataset_name=output_dataset_name,
+    )
+    output_dir = _ensure_output_dir(output_dir)
+
+    try:
+        detector = ImageObjectDetectionBoundingBox(
+            modelSize=model_size,
+            confThreshold=conf_threshold,
+            targetClasses=target_classes,
+            outputDir=output_dir,
+        )
+    except Exception as e:
+        logger.error("Failed to init YOLO detector for task {}: {}", task_id, e)
+        _update_task_status(
+            task_id,
+            status="failed",
+            total_images=total_images,
+            processed_images=0,
+            detected_objects=0,
+            error_message=f"Init YOLO detector failed: {e}",
+        )
+        return
+
+    processed = 0
+    detected_total = 0
+
+    for file_path, file_name in files:
+        try:
+            sample = {
+                "image": file_path,
+                "filename": file_name,
+            }
+            result = detector.execute(sample)
+
+            annotations = (result or {}).get("annotations", {})
+            detections = annotations.get("detections", [])
+            detected_total += len(detections)
+            processed += 1
+
+            progress = int(processed * 100 / total_images) if total_images > 0 else 100
+
+            _update_task_status(
+                task_id,
+                status="running",
+                progress=progress,
+                processed_images=processed,
+                detected_objects=detected_total,
+                total_images=total_images,
+                output_path=output_dir,
+            )
+        except Exception as e:
+            logger.error(
+                "Failed to process image for task {}: file_path={}, error={}",
+                task_id,
+                file_path,
+                e,
+            )
+            continue
+
+    _update_task_status(
+        task_id,
+        status="completed",
+        progress=100,
+        processed_images=processed,
+        detected_objects=detected_total,
+        total_images=total_images,
+        output_path=output_dir,
+        completed=True,
+    )
+
+    logger.info(
+        "Completed auto-annotation task: id={}, total_images={}, processed={}, detected_objects={}, output_path={}",
+        task_id,
+        total_images,
+        processed,
+        detected_total,
+        output_dir,
+    )
+
+    if output_dataset_name and output_dataset_id:
+        try:
+            _register_output_dataset(
+                task_id=task_id,
+                output_dataset_id=output_dataset_id,
+                output_dir=output_dir,
+                output_dataset_name=output_dataset_name,
+                total_images=total_images,
+            )
+        except Exception as e:  # pragma: no cover - 防御性日志
+            logger.error(
+                "Failed to register auto-annotation output as dataset for task {}: {}",
+                task_id,
+                e,
+            )
+
+
+def _worker_loop() -> None:
+    """Worker 主循环，在独立线程中运行。"""
+
+    logger.info(
+        "Auto-annotation worker started with poll interval {} seconds, output root {}",
+        POLL_INTERVAL_SECONDS,
+        DEFAULT_OUTPUT_ROOT,
+    )
+
+    while True:
+        try:
+            task = _fetch_pending_task()
+            if not task:
+                time.sleep(POLL_INTERVAL_SECONDS)
+                continue
+
+            _process_single_task(task)
+        except Exception as e:  # pragma: no cover - 防御性日志
+            logger.error("Auto-annotation worker loop error: {}", e)
+            time.sleep(POLL_INTERVAL_SECONDS)
+
+
+def start_auto_annotation_worker() -> None:
+    """在后台线程中启动自动标注 worker。"""
+
+    thread = threading.Thread(target=_worker_loop, name="auto-annotation-worker", daemon=True)
+    thread.start()
+    logger.info("Auto-annotation worker thread started: {}", thread.name)

runtime/python-executor/datamate/operator_runtime.py

@@ -13,6 +13,7 @@ from datamate.common.error_code import ErrorCode
 from datamate.scheduler import cmd_scheduler
 from datamate.scheduler import func_scheduler
 from datamate.wrappers import WRAPPERS
+from datamate.auto_annotation_worker import start_auto_annotation_worker
 
 # 日志配置
 LOG_DIR = "/var/log/datamate/runtime"
@@ -49,6 +50,16 @@ class APIException(Exception):
         return result
 
 
+@app.on_event("startup")
+async def startup_event():
+    """FastAPI 启动时初始化后台自动标注 worker。"""
+
+    try:
+        start_auto_annotation_worker()
+    except Exception as e:  # pragma: no cover - 防御性日志
+        logger.error("Failed to start auto-annotation worker: {}", e)
+
+
 @app.exception_handler(APIException)
 async def api_exception_handler(request: Request, exc: APIException):
     return JSONResponse(

scripts/db/data-annotation-init.sql

@@ -37,6 +37,30 @@ CREATE TABLE t_dm_labeling_projects (
     INDEX idx_labeling_project_id (labeling_project_id)
 ) COMMENT='标注项目表';
 
+-- 自动标注任务表
+CREATE TABLE t_dm_auto_annotation_tasks (
+    id VARCHAR(36) PRIMARY KEY COMMENT 'UUID',
+    name VARCHAR(255) NOT NULL COMMENT '任务名称',
+    dataset_id VARCHAR(36) NOT NULL COMMENT '数据集ID',
+    dataset_name VARCHAR(255) COMMENT '数据集名称（冗余字段，方便查询）',
+    config JSON NOT NULL COMMENT '任务配置（模型规模、置信度等）',
+    file_ids JSON COMMENT '要处理的文件ID列表，为空则处理数据集所有图像',
+    status VARCHAR(50) NOT NULL DEFAULT 'pending' COMMENT '任务状态: pending/running/completed/failed',
+    progress INT DEFAULT 0 COMMENT '任务进度 0-100',
+    total_images INT DEFAULT 0 COMMENT '总图片数',
+    processed_images INT DEFAULT 0 COMMENT '已处理图片数',
+    detected_objects INT DEFAULT 0 COMMENT '检测到的对象总数',
+    output_path VARCHAR(500) COMMENT '输出路径',
+    error_message TEXT COMMENT '错误信息',
+    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '更新时间',
+    completed_at TIMESTAMP NULL COMMENT '完成时间',
+    deleted_at TIMESTAMP NULL COMMENT '删除时间（软删除）',
+    INDEX idx_dataset_id (dataset_id),
+    INDEX idx_status (status),
+    INDEX idx_created_at (created_at)
+) COMMENT='自动标注任务表';
+
 
 -- 内置标注模板初始化数据
 -- 这些模板将在系统首次启动时自动创建

scripts/images/backend/Dockerfile

@@ -1,5 +1,4 @@
 FROM maven:3-eclipse-temurin-21 AS builder
-
 COPY backend/ /opt/backend
 
 RUN cd /opt/backend/services && \

scripts/images/runtime/Dockerfile

@@ -24,7 +24,6 @@ WORKDIR /opt/runtime
 RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install -e .[all] --system \
     && uv pip install -r /opt/runtime/datamate/ops/pyproject.toml --system \
-    && uv pip uninstall torch torchvision --system \
     && python -m spacy download zh_core_web_sm \
     && echo "/usr/local/lib/ops/site-packages" > /usr/local/lib/python3.11/site-packages/ops.pth