# AI Image Captioner (`seemuapps/image-captioner`) Actor Generate accurate text descriptions for any image using AI — bulk caption product photos, screenshots, or any image URL for SEO, accessibility, and content tagging. - **URL**: https://apify.com/seemuapps/image-captioner.md - **Developed by:** [Andrew](https://apify.com/seemuapps) (community) - **Categories:** E-commerce, SEO tools, Social media - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $12.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## AI Image Captioner Generate accurate, detailed text descriptions for any image using AI — bulk caption product photos, screenshots, or any image URL for SEO alt text, accessibility compliance, and content tagging. ### What you get - Natural-language captions generated by Molmo 2, trained on 712,000+ human-described images - Three detail levels: brief one-liner, balanced description, or full detailed paragraph - Optional focus directive to target specific aspects (text, background, faces, objects, etc.) - One output record per image with the caption and source URL - Supports bulk processing — pass up to 50 image URLs and get all captions in a single run - Export to JSON or CSV directly from the Apify console ### Use cases - **E-commerce SEO** — generate alt text for thousands of product images automatically - **Accessibility compliance** — add descriptive alt text to images on websites and apps - **Content moderation** — understand what's in user-uploaded images before publishing - **Dataset labeling** — annotate image datasets for machine learning pipelines - **Digital asset management** — auto-tag and describe photos in large media libraries - **Social media monitoring** — caption scraped images to make them searchable by content ### Examples | Image | Detail Level | Caption | |-------|-------------|---------| | ![Nike sneaker on red background](https://i.imgur.com/JnmcDHP.jpeg) | **High** | A vibrant red Nike sneaker takes center stage in this striking advertisement, set against a bold red background that creates a visually cohesive and eye-catching composition. The shoe is positioned at an angle, giving the impression of motion and energy. The sneaker features a white Nike swoosh, darker red laces, and "Nike Free" branding on the white sole. The lighting is bright and even, highlighting the shoe's textures and details. | | ![Nike sneaker on red background](https://i.imgur.com/JnmcDHP.jpeg) | **Medium** | A vibrant red Nike sneaker is displayed against a matching red background. The shoe features a white Nike swoosh and "Nike Free" branding on the sole. The laces are a darker shade of red, complementing the overall design. | | ![Nike sneaker on red background](https://i.imgur.com/JnmcDHP.jpeg) | **Low** | A red Nike sneaker with white accents. | | ![Food flatlay with three dishes](https://i.imgur.com/GdMlOMj.jpeg) | **High** | A top-down view of a rustic wooden table with three round bowls arranged in a triangular formation. The central bowl features slices of medium-rare steak garnished with fresh green leaves and a red chili pepper. The left bowl holds crispy fried fish topped with a creamy sauce and herbs. The right bowl contains a meat dish garnished with thinly sliced red onions and nuts. Scattered around the bowls are whole chili peppers, cashews, and a small bowl of brown dipping sauce. | | ![Food flatlay with three dishes](https://i.imgur.com/GdMlOMj.jpeg) | **Medium** | Three bowls of food are arranged on a gray wooden table, creating a rustic dining scene. The central bowl contains sliced steak, while the left bowl holds fried fish topped with sauce and herbs. The right bowl features a meat dish garnished with onions and nuts. | | ![Food flatlay with three dishes](https://i.imgur.com/GdMlOMj.jpeg) | **Low** | Three bowls of food on a wooden table with garnishes. | ### How to use 1. Paste one or more image URLs into the **Images** field (or upload files directly) 2. Choose a **Detail Level** — High gives the most descriptive output (recommended for SEO and accessibility) 3. Optionally add a **Focus** hint to direct the model's attention (e.g. "describe only the text visible") 4. Click **Run** — captions appear in the **Dataset** tab when complete 5. Export results as JSON or CSV, or connect to downstream actors via the Apify API ### Output format Each dataset record: ```json { "inputImageUrl": "https://example.com/product.jpg", "caption": "A white ceramic coffee mug sitting on a wooden table next to an open laptop. The mug has a minimalist logo on the front and steam rising from the top, suggesting the coffee is hot.", "detailLevel": "high", "status": "success", "error": null } ```` ### Input options | Field | Type | Description | |-------|------|-------------| | Images | URL list | One or more `http/https` image URLs or base64 data URIs | | Upload Images | File upload | Upload images directly from your computer | | Detail Level | Select | `Low` (one-liner), `Medium` (balanced), `High` (detailed paragraph) — default: High | | Focus | Text | Optional directive to focus the caption on a specific aspect of the image | ### Limits - Maximum 50 images per run - Each image must be a publicly accessible URL or a base64 data URI - Processing time is typically 5–15 seconds per image # Actor input Schema ## `images` (type: `array`): Images to caption. Each entry must be a publicly accessible http/https URL or a base64 data URI (data:image/jpeg;base64,...). ## `imageFiles` (type: `array`): Upload image files directly. Combined with any URLs provided above. ## `detailLevel` (type: `string`): How much detail to include in the caption. High produces the most descriptive output. ## `focus` (type: `string`): Optionally direct the model's attention to a specific aspect of the image — e.g. 'describe only the text visible' or 'focus on the background environment'. ## Actor input object example ```json { "images": [ "https://i.imgur.com/TSem3Jf.jpeg", "https://i.imgur.com/or3U2Xx.jpeg" ], "detailLevel": "high" } ``` # Actor output Schema ## `results` (type: `string`): One record per image: inputImageUrl, caption, detailLevel, status (success|error), error. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "images": [ "https://i.imgur.com/TSem3Jf.jpeg", "https://i.imgur.com/or3U2Xx.jpeg" ], "focus": "" }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/image-captioner").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "images": [ "https://i.imgur.com/TSem3Jf.jpeg", "https://i.imgur.com/or3U2Xx.jpeg", ], "focus": "", } # Run the Actor and wait for it to finish run = client.actor("seemuapps/image-captioner").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "images": [ "https://i.imgur.com/TSem3Jf.jpeg", "https://i.imgur.com/or3U2Xx.jpeg" ], "focus": "" }' | apify call seemuapps/image-captioner --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/image-captioner", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Image Captioner", "description": "Generate accurate text descriptions for any image using AI — bulk caption product photos, screenshots, or any image URL for SEO, accessibility, and content tagging.", "version": "1.0", "x-build-id": "zdJQCrjQG8ZFRBYX7" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~image-captioner/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-image-captioner", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~image-captioner/runs": { "post": { "operationId": "runs-sync-seemuapps-image-captioner", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~image-captioner/run-sync": { "post": { "operationId": "run-sync-seemuapps-image-captioner", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "images": { "title": "Images", "type": "array", "description": "Images to caption. Each entry must be a publicly accessible http/https URL or a base64 data URI (data:image/jpeg;base64,...).", "items": { "type": "string" } }, "imageFiles": { "title": "Upload Images", "type": "array", "description": "Upload image files directly. Combined with any URLs provided above.", "items": { "type": "string" } }, "detailLevel": { "title": "Detail Level", "enum": [ "low", "medium", "high" ], "type": "string", "description": "How much detail to include in the caption. High produces the most descriptive output.", "default": "high" }, "focus": { "title": "Focus (optional)", "type": "string", "description": "Optionally direct the model's attention to a specific aspect of the image — e.g. 'describe only the text visible' or 'focus on the background environment'." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```