--- 格式版本: 2 标题: "OCI AI Vision Custom Model Training (Using OCI Redwood Console) | cloud-infrastructure" 原文链接: "https://blogs.oracle.com/cloud-infrastructure/oci-ai-vision-custom-model-training-label-studio" 发布日期: "2026-08-25" 发布时间校准状态: "found" 发布时间需复核: "否" 发布时间来源: "rule:configured_publication_date_rule" 发布时间证据: "doc-fixed-49fb23095330-publication-date html:original: August 25, 2026" 发布时间校准原因: "信源发布日期识别规则直接确认发布时间" 发布时间校准置信度: "high" 发布时间候选数量: 1 发布时间严格候选数量: 1 发布时间原页读取状态: "source template page reused from URL open" 发布时间未找到原因: "" 发布时间校准时间: "2026-08-26T16:00:05+08:00" 发布时间仲裁状态: "skipped" 发布时间仲裁尝试次数: 0 发布时间仲裁耗时毫秒: 0 发现时间: "2026-08-26T15:59:29+08:00" 入库时间: "2026-08-26T08:00:28.490Z" 来源平台: "固定入口" 搜索渠道: "fixed_url" 搜索词: "https://blogs.oracle.com/?page=news" 匹配关键词: - "AI" 相关厂家: - "Oracle" 相关专家: [] 内容类型: "网页" 抓取工具: "CDP Render" 清洗工具: "CDP Text + Defuddle/Readability 正文提取" 原始附件: [] 图片摘要: - "✓ ./assets/img-df2c2075.png | other | " - "✓ ./assets/img-1d98ee2e.png | other | " - "✓ ./assets/img-fb9e0fef.png | other | " - "✓ ./assets/img-607fcf8d.png | other | " - "✓ ./assets/img-c0d1f07b.png | other | " - "✓ ./assets/img-d7e5c365.png | other | " - "✓ ./assets/img-46c24caa.png | other | " - "✓ ./assets/img-04abdf96.png | other | " AI优质: "否" AI打分: 27 AI分档: "非优质" AI质检状态: "不通过" AI打分理由: "正文主线是使用Label Studio标注数据并在OCI AI Vision中训练、测试自定义目标检测模型的操作教程，细节集中于控制台步骤、IAM权限和数据格式转换，不涉及机架级AI架构、互连、供电、液冷或RAS。来源为Oracle官方博客，正文较完整，但发布日期缺失；相较知识库中的OCI机架级平台信息，仅新增对Label Studio训练流程的应用层说明，未提供新机架产品、标准、工程实测或部署事实。命中“教程与运维选型”及“应用与模型效率”硬否决项。" AI质检模型: "gpt-5.6-sol" AI质检时间: "2026-08-26T16:00:40+08:00" AI主题相关性: 1 AI来源权威性: 10 AI新颖性: 4 AI技术细节: 3 AI商业部署信号: 0 AI完整性: 9 AI评分提示词版本: "v17-精简生产版" AI评分提示词SHA256: "48fb9777f386026761b4873eaff30807694fb11e9b352d7c69bf2dfde750cc7d" AI评分知识库版本: "knowledge_base_v1-20260819" AI评分知识库SHA256: "e8daaadd1f28923bf4557a3e84042f83b8070615c03a40d448c106e6bfe5fcdc" AI评分知识库检索词: "[\"Oracle\",\"https://blogs.oracle.com/?page=news\",\"OCI\",\"AI-Vision\",\"OD\",\"ID\",\"URL\",\"JSON\",\"assets/img-df2c2075.png\",\"assets/img-1d98ee2e.png\",\"assets/img-fb9e0fef.png\",\"assets/img-607fcf8d.png\"]" AI评分知识库命中: "[{\"id\":\"historical-may-011\",\"title\":\"不只卖GPU！英伟达向OpenAI、Anthropic、SpaceX与甲骨文交付首批Vera CPU\",\"sourceType\":\"curated_item\",\"time\":\"2026-05\",\"matchedTerms\":[\"OCI\"],\"rank\":-7.166646699263573},{\"id\":\"historical-jun-014\",\"title\":\"NVIDIA发布Vera CPU Rack整机柜方案，面向AI工厂中的Agentic AI与强化学习负载\",\"sourceType\":\"curated_item\",\"time\":\"2026-06\",\"matchedTerms\":[\"Oracle\",\"ID\"],\"rank\":-6.113711136057652},{\"id\":\"july-correct-0015\",\"title\":\"AMD to join the optical interconnect party with 2027 Instinct GPUs\",\"sourceType\":\"labeled_article\",\"time\":\"2026-07\",\"matchedTerms\":[\"OCI\",\"OD\",\"ID\",\"URL\"],\"rank\":-5.985482973642268},{\"id\":\"july-correct-0017\",\"title\":\"AMD challenges Nvidia’s networking dominance with Helios racks boasting 50% higher bandwidth\",\"sourceType\":\"labeled_article\",\"time\":\"2026-07\",\"matchedTerms\":[\"Oracle\",\"OCI\",\"OD\",\"ID\",\"URL\"],\"rank\":-5.625102012640354},{\"id\":\"july-correct-0018\",\"title\":\"AMD, Cerebras partner on joint Helios rack-scale AI inference platform\",\"sourceType\":\"labeled_article\",\"time\":\"2026-07\",\"matchedTerms\":[\"Oracle\",\"OD\",\"ID\",\"URL\"],\"rank\":-5.509000993021489}]" AI摘要: "OCI AI Vision 弃用原有数据标注服务后，新增支持通过开源工具 Label Studio 完成自定义模型训练。" AI摘要模型: "ali-deepseek-v4-flash" AI摘要时间: "2026-09-07T02:08:46.617Z" 采集批次: "2026年8月26日15点57分35秒" 采集批次ID: "20260826-155735-186" 去重键: "https://blogs.oracle.com/cloud-infrastructure/oci-ai-vision-custom-model-training-label-studio" --- After deprication of data labeling service, OCI introduced the much awaited feature of supporting label studio for custom model training in AI-Vision. Here is the detailed process. ### Step 1: Installing Label Studio Label studio is an open source software which you can install from various ways mentioned in there official documentation. Please refer to the same: [https://labelstud.io/guide/install.html](https://labelstud.io/guide/install.html#Install-using-pip) I personally prefer brew way for my mac. Terminal window showing the Homebrew command to install Label Studio on macOS. ### Step 2: Running label studio on local host and creating project Your application will start default on [http://localhost:8081/](http://localhost:8081/) Click on C **reate Project** to start creating the project and add project details Label Studio home page with the Create Project button highlighted. Go to data import and import all the images from your local or URL For demonstrations, used synthetic or appropriately authorized data. Go to labeling setup and select object detection for bouding box *Note: Select image classification in case you want to train an image classification model.* Press enter or click to view image in full size Label Studio Labeling Setup page with object detection selected for bounding-box annotation. Provide your labelset you will use to annotate and click on save Label Studio labeling configuration showing a label set before saving. ### Step 3: Click on label all task and start labeling Label Studio task list showing image thumbnails awaiting annotation. Start annotating each of the image (make sure each label will have atleast 10 images/ dataset minimum) For demonstrations, used synthetic or appropriately authorized data. Once annotated, download the file as JSON ![Label Studio export dialog showing annotations downloaded in JSON format.](./assets/img-df2c2075.png) Label Studio export dialog showing annotations downloaded in JSON format. ### Step 4: Open OCI console and Train Custom OD Model Open OCI console and go to vision service. Click on Create Project to start creating the project ![OCI Vision service page with the Create Project option.](./assets/img-1d98ee2e.png) OCI Vision service page with the Create Project option. Fill all the required details. OCI Vision Create Project form with required project details. Note: As a pre-requisite, make sure you have all the permission added. Below are the list of permission required: Before you start creating a custom vision model, complete the following setup tasks. If you don’t have permission to create these policies, your tenancy administrator should create them for you. **1: Create a group** *Create a group for your users.* *Add users to the group.* **2: Create policies** Create a policy in the root compartment with the following statements: **2.1 Policy to allow group access to AI Vision service** ``` allow group to manage ai-service-vision-family in tenancy ``` **2.2 Policy to allow group access to files in Object Storage** ``` allow group to use object-family in tenancy ``` Once project is created, go inside the project and click on model tab. ![OCI Vision project page with the Models tab selected.](./assets/img-fb9e0fef.png) OCI Vision project page with the Models tab selected. Click on create dataset, and select model type, object storage where you want to store your annotated file + all the dataset. ![OCI Vision Create Dataset form for choosing the model type and Object Storage location.](./assets/img-607fcf8d.png) OCI Vision Create Dataset form for choosing the model type and Object Storage location. Choose thw exported annotated file and data file and click on upload dataset. This will upload all your dataset to mentioned object storage and also convert label studio JSON data to JSONL and upload that to the same location. ![OCI Vision Upload Dataset page for selecting annotated data and image files.](./assets/img-c0d1f07b.png) OCI Vision Upload Dataset page for selecting annotated data and image files. Once uploaded you can check the same in object storage. After that, click on create model to start creating the model and fill all the required details. Select the same converted JSONL (with name object-detection\_dataset.jsonl ![OCI Vision Create Model form showing the converted object-detection dataset JSONL file.](./assets/img-d7e5c365.png) OCI Vision Create Model form showing the converted object-detection dataset JSONL file. Provide model name and training type ![OCI Vision model configuration form for entering a model name and training type.](./assets/img-46c24caa.png) OCI Vision model configuration form for entering a model name and training type. Review and click on create to start creating the model. ![OCI Vision review page with the Create button to start model training.](./assets/img-04abdf96.png) OCI Vision review page with the Create button to start model training. Oncle model is active, click on infernce and start testing the model. OCI Vision custom model testing screen showing detected fields and confidence scores for a redacted sample ID card.