> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/roboflow/roboflow-hi/deploy/supported-models/sam3.md). # SAM3 हम Meta के [Segment Anything Model 3](https://github.com/facebookresearch/sam3) हमारे inference के माध्यम से [Serverless Hosted API](/roboflow/roboflow-hi/deploy/serverless-hosted-api-v2.md). हम दो अलग-अलग SAM3 endpoints प्रदान करते हैं: {% hint style="info" %} Roboflow पर SAM3 मॉडल को प्रशिक्षित करना सशुल्क [योजनाएँ](/roboflow/roboflow-hi/billing/plans.md) जिनमें शामिल हैं [उपयोग-आधारित बिलिंग](/roboflow/roboflow-hi/billing/credits.md). {% endhint %} * [Promptable concept segmentation](#concept-segmentation-pcs) (**PCS**), जो छवि में किसी concept के हर instance को segment करता है। Concepts को text prompts, exemplar boxes, या दोनों से वर्णित किया जाता है। * [Promptable visual segmentation](#visual-segmentation-pvs) (**PVS**), जो SAM2 की शैली में points या एक box से प्रति अनुरोध एक object को इंटरैक्टिव रूप से segment करता है। endpoint चुनने के लिए इस table का उपयोग करें: | आपके पास है | आप चाहते हैं | का उपयोग करें | | ---------------------------------------------------------- | --------------------------------- | ----------------------- | | एक टेक्स्ट विवरण (उदा.: "person") | हर matching instance के लिए masks | `/sam3/concept_segment` | | एक उदाहरण object के चारों ओर एक box | हर similar instance के लिए masks | `/sam3/concept_segment` | | वस्तुओं को शामिल या बाहर करने के लिए text और example boxes | हर matching instance के लिए masks | `/sam3/concept_segment` | | किसी एक विशिष्ट object पर एक click या box | केवल उस object के लिए एक mask | `/sam3/visual_segment` | अपना [API key](https://app.roboflow.com/settings/api) के रूप में `api_key` हर request पर query parameter के रूप में। ## Concept Segmentation (PCS) `POST https://serverless.roboflow.com/sam3/concept_segment` प्रत्येक entry में `prompts` एक concept का वर्णन करती है। प्रतिक्रिया में एक `prompt_results` प्रत्येक prompt के लिए एक entry, जिसमें पाए गए हर instance शामिल होते हैं। Requests अधिकतम 16 prompts स्वीकार करती हैं। ### टेक्स्ट prompts ```python import os import requests payload = { "image": {"type": "url", "value": "https://media.roboflow.com/inference/people-walking.jpg"}, "prompts": [ {"type": "text", "text": "person"}, {"type": "text", "text": "backpack"}, ], "output_prob_thresh": 0.5, "format": "polygon", # या "rle" } response = requests.post( "https://serverless.roboflow.com/sam3/concept_segment", params={"api_key": os.getenv("API_KEY")}, json=payload, ) for prompt_result in response.json()["prompt_results"]: print(prompt_result["echo"], len(prompt_result["predictions"]), "instances") ``` Images को inline के रूप में भी भेजा जा सकता है `{"type": "base64", "value": ""}`. ### Exemplar box prompts Text के बजाय, आप एक exemplar के साथ prompt कर सकते हैं: एक उदाहरण object के चारों ओर एक box। मॉडल example से मेल खाने वाले हर instance को खोजता है, न कि केवल box किए गए object को। ```python payload = { "image": {"type": "url", "value": "https://media.roboflow.com/inference/people-walking.jpg"}, "prompts": [ { "type": "visual", "boxes": [{"x": 1409, "y": 705, "width": 112, "height": 183}], "box_labels": [1], } ], "output_prob_thresh": 0.5, "format": "polygon", } ``` Boxes absolute pixel coordinates का उपयोग करते हैं। दो formats स्वीकार किए जाते हैं: * `{"x": ..., "y": ..., "width": ..., "height": ...}` जहाँ `x`, `y` top-left corner है * `{"x0": ..., "y0": ..., "x1": ..., "y1": ...}` स्पष्ट corners के लिए `box_labels` आवश्यक है जब `boxes` set हो और प्रति box एक entry होनी चाहिए: `1` एक positive exemplar को दर्शाता है (ऐसी वस्तुओं को खोजें), `0` एक negative exemplar को दर्शाता है (ऐसी वस्तुओं को बाहर रखें)। ### संयुक्त text और exemplar prompts एक single prompt में text और exemplar boxes दोनों हो सकते हैं। यह किसी text concept को visual examples के साथ सीमित करने, या negative exemplars से मिलती-जुलती चीजों को बाहर करने के लिए उपयोगी है: ```python payload = { "image": {"type": "url", "value": "https://media.roboflow.com/inference/people-walking.jpg"}, "prompts": [ { "type": "visual", "text": "person", "boxes": [ {"x": 1409, "y": 705, "width": 112, "height": 183}, {"x": 1216, "y": 496, "width": 124, "height": 184}, ], "box_labels": [1, 0], } ], "output_prob_thresh": 0.5, "format": "polygon", } ``` यहाँ मॉडल पहले (positive) exemplar से मेल खाने वाले लोगों को segment करता है, जबकि दूसरे (negative) exemplar से मिलते-जुलते instances को दबाता है। ## Visual Segmentation (PVS) `POST https://serverless.roboflow.com/sam3/visual_segment` PVS clicks या एक box द्वारा संकेतित एक विशिष्ट object को segment करता है। इसे interactive, human-in-the-loop mask refinement के लिए उपयोग करें; जब आप किसी concept के हर instance चाहते हों, तब PCS का उपयोग करें। ```python import os import requests payload = { "image": {"type": "url", "value": "https://media.roboflow.com/inference/people-walking.jpg"}, "prompts": { "prompts": [ { "points": [{"x": 1465, "y": 796, "positive": True}], "box": {"x": 1465, "y": 796, "width": 112, "height": 183}, } ] }, "multimask_output": False, "format": "json", } response = requests.post( "https://serverless.roboflow.com/sam3/visual_segment", params={"api_key": os.getenv("API_KEY")}, json=payload, ) prediction = response.json()["predictions"][0] print(prediction["confidence"], len(prediction["masks"]), "polygons") ``` एक prompt में शामिल हो सकता है `points`, एक `box`, या दोनों: * `points` absolute pixel coordinates होते हैं। `"positive": true` क्लिक किए गए region को शामिल करता है, `false` उसे बाहर करता है। mask को refine करने के लिए और points जोड़ें। * `box` center-anchored coordinates का उपयोग करता है: `x`, `y` box का केंद्र है, PCS boxes के विपरीत जो top-left anchored होते हैं। प्रतिक्रिया में prompt के लिए केवल सबसे उच्च-confidence mask होता है। `multimask_output` नियंत्रित करता है कि मॉडल कितने internal mask proposals उत्पन्न करता है (true होने पर तीन), लेकिन प्रतिक्रिया के लिए हमेशा सबसे अच्छा proposal चुना जाता है। {% hint style="warning" %} प्रति request एक prompt भेजें। एक PVS request में कई prompts वर्तमान में केवल एक prediction लौटाते हैं। {% endhint %} OpenCV का उपयोग करके एक interactive demo के लिए, यह देखें [GitHub Gist](https://gist.github.com/Erol444/4cbc33c6ac52d83c63f6f9d86ca8a7a4), जिसका उपयोग इस वीडियो में किया गया था: {% embed url="" %} ## Endpoints ## SAM3 PCS (promptable concept segmentation) > \*\*Concept Segmentation (Text Prompts)\*\*\ > \ > Allows you to segment objects using text prompts.\ > \ > \*\*Image Input\*\*: The \`image\` field accepts either:\ > \- \`{"type": "url", "value": "\"}\` - A publicly accessible image URL\ > \- \`{"type": "base64", "value": "\"}\` - Base64 encoded image data\ > \ > \*\*Prompts\*\*: Each prompt in the \`prompts\` array should have \`type: "text"\` and a \`text\` field with the object description. ```json {"openapi":"3.1.0","info":{"title":"Roboflow SAM3 API","version":"0.64.4"},"servers":[{"url":"https://serverless.roboflow.com"}],"paths":{"/sam3/concept_segment":{"post":{"summary":"SAM3 PCS (promptable concept segmentation)","description":"**Concept Segmentation (Text Prompts)**\n\nAllows you to segment objects using text prompts.\n\n**Image Input**: The `image` field accepts either:\n- `{\"type\": \"url\", \"value\": \"\"}` - A publicly accessible image URL\n- `{\"type\": \"base64\", \"value\": \"\"}` - Base64 encoded image data\n\n **Prompts**: Each prompt in the `prompts` array should have `type: \"text\"` and a `text` field with the object description.","operationId":"sam3_segment_image_sam3_concept_segment_post","parameters":[{"name":"api_key","in":"query","required":true,"schema":{"type":"string","title":"API Key"},"description":"Your Roboflow API Key. Get one at https://app.roboflow.com/settings/api"}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/Sam3SegmentationRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/Sam3SegmentationResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}}},"components":{"schemas":{"Sam3SegmentationRequest":{"properties":{"image":{"$ref":"#/components/schemas/InferenceRequestImage","description":"The image to be segmented."},"prompts":{"items":{"$ref":"#/components/schemas/Sam3Prompt"},"type":"array","minItems":1,"title":"Prompts","description":"List of prompts (text and/or visual)"},"format":{"type":"string","title":"Format","description":"One of 'polygon', 'rle'","default":"polygon"},"image_id":{"type":"string","title":"Image Id","description":"Optional ID for caching embeddings."},"output_prob_thresh":{"type":"number","title":"Output Prob Thresh","description":"Score threshold for outputs.","default":0.5},"model_id":{"type":"string","title":"Model Id","description":"The model ID of SAM3. Use 'sam3/sam3_final' to target the generic base model.","default":"sam3/sam3_final"},"nms_iou_threshold":{"type":"number","title":"Nms Iou Threshold","description":"IoU threshold for cross-prompt NMS. If not set, NMS is disabled. Must be in [0.0, 1.0] when set."}},"type":"object","required":["image","prompts"],"title":"Sam3SegmentationRequest"},"InferenceRequestImage":{"properties":{"type":{"type":"string","title":"Type","description":"The type of image data provided, one of `url`, `base64`"},"value":{"type":"string","title":"Value","description":"Image data corresponding to the image type, if type = 'url' then value is a string containing the url of an image, else if type = 'base64' then value is a string containing base64 encoded image data."}},"type":"object","required":["type"],"title":"InferenceRequestImage","description":"Image data for inference request.\n\nAttributes:\n type (str): The type of image data provided, one of 'url', 'base64', or 'numpy'.\n value (Optional[Any]): Image data corresponding to the image type."},"Sam3Prompt":{"properties":{"type":{"type":"string","title":"Type","description":"Hint: `text` or `visual`"},"text":{"type":"string","title":"Text","description":"Text prompt describing the object to segment"},"output_prob_thresh":{"type":"number","title":"Output Prob Thresh","description":"Score threshold for this prompt's outputs. Overrides request-level threshold if set."},"boxes":{"items":{"anyOf":[{"$ref":"#/components/schemas/Box"},{"$ref":"#/components/schemas/BoxXYXY"}]},"type":"array","title":"Boxes","description":"Absolute pixel boxes as either XYWH or XYXY entries"},"box_labels":{"items":{"anyOf":[{"type":"integer"},{"type":"boolean"}]},"type":"array","title":"Box Labels","description":"List of 0/1 or booleans for boxes"}},"type":"object","required":["type"],"title":"Sam3Prompt","description":"Unified prompt that can contain text and/or geometry. Absolute pixel coordinates are used for boxes."},"Sam3SegmentationResponse":{"properties":{"prompt_results":{"items":{"$ref":"#/components/schemas/Sam3PromptResult"},"type":"array","title":"Prompt Results","description":"Results for each prompt in the request"},"time":{"type":"number","title":"Time","description":"The time in seconds it took to produce the segmentation including preprocessing"}},"type":"object","required":["prompt_results","time"],"title":"Sam3SegmentationResponse"},"Sam3PromptResult":{"properties":{"prompt_index":{"type":"integer","title":"Prompt Index","description":"Index of the prompt this result corresponds to"},"echo":{"$ref":"#/components/schemas/Sam3PromptEcho","description":"Echo of the original prompt for reference"},"predictions":{"items":{"$ref":"#/components/schemas/Sam3SegmentationPrediction"},"type":"array","title":"Predictions","description":"Segmentation predictions for this prompt"}},"type":"object","required":["prompt_index","predictions"],"title":"Sam3PromptResult"},"Sam3PromptEcho":{"properties":{"prompt_index":{"type":"integer","title":"Prompt Index"},"type":{"type":"string","title":"Type","description":"The prompt type (`text` or `visual`)"},"text":{"type":"string","title":"Text","description":"The text prompt if type is `text`"},"num_boxes":{"type":"integer","title":"Num Boxes","description":"Number of bounding boxes in the prompt"}},"type":"object","title":"Sam3PromptEcho"},"Sam3SegmentationPrediction":{"properties":{"format":{"type":"string","title":"Format","description":"The format of the mask data, either `polygon` or `rle`"},"confidence":{"type":"number","title":"Confidence","description":"Confidence score for this prediction"},"masks":{"items":{"items":{"items":{"type":"number"},"type":"array","minItems":2,"maxItems":2},"type":"array"},"type":"array","title":"Masks","description":"Array of polygons, each polygon is an array of [x, y] coordinate points"}},"type":"object","required":["format","confidence","masks"],"title":"Sam3SegmentationPrediction"},"HTTPValidationError":{"properties":{"detail":{"items":{"$ref":"#/components/schemas/ValidationError"},"type":"array","title":"Detail"}},"type":"object","title":"HTTPValidationError"},"ValidationError":{"properties":{"loc":{"items":{"anyOf":[{"type":"string"},{"type":"integer"}]},"type":"array","title":"Location"},"msg":{"type":"string","title":"Message"},"type":{"type":"string","title":"Error Type"}},"type":"object","required":["loc","msg","type"],"title":"ValidationError"}}}} ``` ## SAM3 PVS (promptable visual segmentation) > \*\*Interactive Segmentation (SAM 2 Style)\*\*\ > \ > SAM 3 also supports interactive segmentation using points and boxes.\ > \ > \*\*Image Input\*\*: The \`image\` field accepts either:\ > \- \`{"type": "url", "value": "\"}\` - A publicly accessible image URL\ > \- \`{"type": "base64", "value": "\"}\` - Base64 encoded image data\ > \ > \> \*\*Note\*\*: NumPy arrays are NOT supported on the serverless API. Use URL or base64 encoding only.\ > \ > \*\*Prompts\*\*: Support point-based prompts with positive/negative clicks for interactive segmentation. ```json {"openapi":"3.1.0","info":{"title":"Roboflow SAM3 API","version":"0.64.4"},"servers":[{"url":"https://serverless.roboflow.com"}],"paths":{"/sam3/visual_segment":{"post":{"summary":"SAM3 PVS (promptable visual segmentation)","description":"**Interactive Segmentation (SAM 2 Style)**\n\nSAM 3 also supports interactive segmentation using points and boxes.\n\n**Image Input**: The `image` field accepts either:\n- `{\"type\": \"url\", \"value\": \"\"}` - A publicly accessible image URL\n- `{\"type\": \"base64\", \"value\": \"\"}` - Base64 encoded image data\n\n> **Note**: NumPy arrays are NOT supported on the serverless API. Use URL or base64 encoding only.\n\n**Prompts**: Support point-based prompts with positive/negative clicks for interactive segmentation.","operationId":"sam3_visual_segment_sam3_visual_segment_post","parameters":[{"name":"api_key","in":"query","required":true,"schema":{"type":"string","title":"API Key"},"description":"Your Roboflow API Key. Get one at https://app.roboflow.com/settings/api"}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/Sam2SegmentationRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/Sam2SegmentationResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}}},"components":{"schemas":{"Sam2SegmentationRequest":{"properties":{"image":{"$ref":"#/components/schemas/InferenceRequestImage","description":"The image to be segmented."},"image_id":{"type":"string","title":"Image Id","description":"The ID of the image to be segmented used to retrieve cached embeddings. If an embedding is cached, it will be used instead of generating a new embedding. If no embedding is cached, a new embedding will be generated and cached."},"prompts":{"$ref":"#/components/schemas/Sam2PromptSet","description":"A list of prompts for masks to predict. Each prompt can include a bounding box and / or a set of postive or negative points."},"format":{"type":"string","title":"Format","description":"The format of the response. Must be one of 'json', 'rle', or 'binary'. If binary, masks are returned as binary numpy arrays. If json, masks are converted to polygons. If rle, masks are converted to RLE format.","default":"json"},"sam2_version_id":{"type":"string","title":"Sam2 Version Id","description":"The version ID of SAM to be used for this request. Must be one of hiera_tiny, hiera_small, hiera_large, hiera_b_plus","default":"hiera_large"},"multimask_output":{"type":"boolean","title":"Multimask Output","description":"If true, the model will return three masks. For ambiguous input prompts (such as a single click), this will often produce better masks than a single prediction.","default":true},"save_logits_to_cache":{"type":"boolean","title":"Save Logits To Cache","description":"If True, saves the low-resolution logits to the cache for potential future use.","default":false},"load_logits_from_cache":{"type":"boolean","title":"Load Logits From Cache","description":"If True, attempts to load previously cached low-resolution logits for the given image and prompt set.","default":false}},"type":"object","required":["image"],"title":"Sam2SegmentationRequest","description":"SAM2 visual segmentation request."},"InferenceRequestImage":{"properties":{"type":{"type":"string","title":"Type","description":"The type of image data provided, one of `url`, `base64`"},"value":{"type":"string","title":"Value","description":"Image data corresponding to the image type, if type = 'url' then value is a string containing the url of an image, else if type = 'base64' then value is a string containing base64 encoded image data."}},"type":"object","required":["type"],"title":"InferenceRequestImage","description":"Image data for inference request.\n\nAttributes:\n type (str): The type of image data provided, one of 'url', 'base64', or 'numpy'.\n value (Optional[Any]): Image data corresponding to the image type."},"Sam2SegmentationResponse":{"properties":{"prompt_results":{"items":{"$ref":"#/components/schemas/Sam2PromptResult"},"type":"array","title":"Prompt Results","description":"Results for each prompt in the request"},"time":{"type":"number","title":"Time","description":"The time in seconds it took to produce the segmentation including preprocessing"}},"type":"object","required":["prompt_results","time"],"title":"Sam2SegmentationResponse"},"Sam2PromptResult":{"properties":{"prompt_index":{"type":"integer","title":"Prompt Index","description":"Index of the prompt this result corresponds to"},"predictions":{"items":{"$ref":"#/components/schemas/Sam2SegmentationPrediction"},"type":"array","title":"Predictions","description":"Segmentation predictions for this prompt"}},"type":"object","required":["prompt_index","predictions"],"title":"Sam2PromptResult"},"HTTPValidationError":{"properties":{"detail":{"items":{"$ref":"#/components/schemas/ValidationError"},"type":"array","title":"Detail"}},"type":"object","title":"HTTPValidationError"},"ValidationError":{"properties":{"loc":{"items":{"anyOf":[{"type":"string"},{"type":"integer"}]},"type":"array","title":"Location"},"msg":{"type":"string","title":"Message"},"type":{"type":"string","title":"Error Type"}},"type":"object","required":["loc","msg","type"],"title":"ValidationError"}}}} ``` --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.roboflow.com/roboflow/roboflow-hi/deploy/supported-models/sam3.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.