# Image Semantic Filter (`bfarrington/image-semantic-filter`) Actor Easily search a dataset of images with natural language queries. E.g. Searching a dataset of street view images for construction sites or social media images for products. - **URL**: https://apify.com/bfarrington/image-semantic-filter.md - **Developed by:** [Ben Farrington](https://apify.com/bfarrington) (community) - **Categories:** AI, Agents, Social media - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.50 / 1,000 image classifieds 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 ## Image Semantic Filter — keep only the images that match your description **Filter any set of images by meaning.** Give this actor a list of image URLs — or point it at another Actor's dataset — and a plain-English description. It scores every image on how well it matches, using CLIP AI vision, and returns only the ones you want, each with a match rating. No API key — you pay a small flat fee per image classified. ```` "a red sports car" → keep car photos that are actually red sports cars "person wearing a hat" → filter a scraped photo set down to hatted people "food on a plate" → pull the food shots out of a mixed image dump "logo on a white background" → find clean product images among the noise ```` ### Why this actor? - ✅ **No API key, no setup** — nothing to configure, no third-party billing account, no quota - ✅ **Simple pay-per-event pricing** — a small flat fee per image classified, billed by Apify; no external per-image API charges, scoring runs on a local AI model - ✅ **Chains off any scraper** — feed it the dataset from an Instagram, e-commerce, or Google Images Actor and curate the output in one step - ✅ **Semantic, not keyword** — CLIP understands objects, colors, scenes and concepts ("something to sit on" finds chairs and benches) - ✅ **Keeps your data** — every original field of a source-dataset item is carried through to the results, plus a `match` rating ### What can you use it for? - 🛒 **Curate scraped product images** — keep only photos matching a style, angle, or setting - 🧹 **Clean image dumps** — filter a mixed set down to the subject you care about - 🖼️ **Moderate & sort** — bucket a large image set by how well it matches a description - 🔎 **Find needles** — locate the few on-target images inside thousands of scraped URLs ### How it works 1. **You provide** a description plus images — a list of URLs, a source dataset ID, or both 2. **AI ranking** — a CLIP vision model scores every image against your description (semantic matching, not keywords) 3. **You receive** the matching images as dataset items, each with a `match` rating (`match`, `ambiguous`, or `no_match`). Set a minimum match level to drop the rest automatically ### Input | Field | Default | Description | |---|---|---| | `searchPrompt` | — | What to look for, in plain English | | `imageUrls` | `[]` | Direct image URLs to score | | `datasetId` | — | Apify dataset (e.g. a scraper's output) to read image URLs from | | `imageUrlField` | `imageUrl` | Field in each source item holding the URL (supports nested `image.url`) | | `minMatch` | `any` | Keep only images at this level or better (`any` / `ambiguous` / `match`) | | `maxImages` | 1000 | Maximum images to download and score | Provide `imageUrls`, a `datasetId`, or both. ### Output One dataset item per kept image, streamed in as it's scored. When the image came from a source dataset, the original item's fields are included alongside the match rating. ```json { "imageUrl": "https://example.com/photos/car-42.jpg", "match": "match" } ```` Export to JSON, CSV, or Excel. Sort or filter on the `match` field to get the strongest matches first. ### FAQ **Where do the images come from?** From you — either a list of URLs or another Apify Actor's dataset. This Actor doesn't scrape or store images; it reads the URLs you provide and returns the ones that match. **What do the match ratings mean?** `match` means a confident visual match; `ambiguous` is borderline and worth a look; `no_match` probably doesn't contain what you described. Use `minMatch` to drop everything below a level, or keep all and filter the dataset yourself. **Can I filter another Actor's results?** Yes — that's the main use. Run a scraper, then pass its dataset ID here with `imageUrlField` set to the field holding the image URL. Every original field is carried through, so the output is your scraped data minus the images that don't match. **Does it re-host the images?** No. The output references your original image URLs, so make sure they stay reachable. Images behind expiring/signed links may not load later. **Can I search for multiple things?** One description per run. Start parallel runs for multiple descriptions over the same images. **How am I charged?** Pay-per-event: one flat charge per image classified. Every image that gets scored counts, including ones filtered out by `minMatch` — you're paying for the AI classification, not just the kept results. Cap a run's cost with `maxImages`. *** *semantic image search · CLIP visual filter · image dataset curation · filter scraped images by content · AI image classification by prompt* # Actor input Schema ## `searchPrompt` (type: `string`): Plain-language description of the images you want to keep, e.g. "a red sports car", "person wearing a hat", "food on a plate". ## `imageUrls` (type: `array`): List of direct image URLs to score. Use this, a source dataset, or both. ## `datasetId` (type: `string`): ID of an Apify dataset (e.g. the output of a scraper) to read image URLs from. Every original field of each item is carried through to the results. ## `imageUrlField` (type: `string`): Name of the field in each source-dataset item that holds the image URL. Supports nested fields with dots, e.g. "image.url". ## `minMatch` (type: `string`): Only keep images at this match level or better. "Keep all" returns every image with its match rating. ## `maxImages` (type: `integer`): Maximum number of images to download and score. Keeps runs bounded on large inputs. ## Actor input object example ```json { "searchPrompt": "a dog", "imageUrls": [], "imageUrlField": "imageUrl", "minMatch": "any", "maxImages": 1000 } ``` # Actor output Schema ## `results` (type: `string`): No description ## `resultsCsv` (type: `string`): No description ## `run` (type: `string`): No description # 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 = { "searchPrompt": "a dog", "imageUrls": [] }; // Run the Actor and wait for it to finish const run = await client.actor("bfarrington/image-semantic-filter").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 = { "searchPrompt": "a dog", "imageUrls": [], } # Run the Actor and wait for it to finish run = client.actor("bfarrington/image-semantic-filter").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 '{ "searchPrompt": "a dog", "imageUrls": [] }' | apify call bfarrington/image-semantic-filter --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=bfarrington/image-semantic-filter", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Image Semantic Filter", "description": "Easily search a dataset of images with natural language queries. E.g. Searching a dataset of street view images for construction sites or social media images for products.", "version": "0.1", "x-build-id": "DKqwJA7CiAZ0tScwC" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/bfarrington~image-semantic-filter/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-bfarrington-image-semantic-filter", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/bfarrington~image-semantic-filter/runs": { "post": { "operationId": "runs-sync-bfarrington-image-semantic-filter", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/bfarrington~image-semantic-filter/run-sync": { "post": { "operationId": "run-sync-bfarrington-image-semantic-filter", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "searchPrompt" ], "properties": { "searchPrompt": { "title": "What to look for", "type": "string", "description": "Plain-language description of the images you want to keep, e.g. \"a red sports car\", \"person wearing a hat\", \"food on a plate\"." }, "imageUrls": { "title": "Image URLs", "type": "array", "description": "List of direct image URLs to score. Use this, a source dataset, or both." }, "datasetId": { "title": "Source dataset ID", "type": "string", "description": "ID of an Apify dataset (e.g. the output of a scraper) to read image URLs from. Every original field of each item is carried through to the results." }, "imageUrlField": { "title": "Image URL field", "type": "string", "description": "Name of the field in each source-dataset item that holds the image URL. Supports nested fields with dots, e.g. \"image.url\".", "default": "imageUrl" }, "minMatch": { "title": "Minimum match level", "enum": [ "any", "ambiguous", "match" ], "type": "string", "description": "Only keep images at this match level or better. \"Keep all\" returns every image with its match rating.", "default": "any" }, "maxImages": { "title": "Max images", "minimum": 1, "type": "integer", "description": "Maximum number of images to download and score. Keeps runs bounded on large inputs.", "default": 1000 } } }, "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 } } } } } } } } } } ```