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Meta SAM 3 is a vision model for Promptable Concept Segmentation (PCS): give it a short text phrase, an image exemplar, or a visual prompt, and it can find, segment, and identify matching object instances across an image or video. Unlike traditional detectors limited to fixed categories—and earlier SAM models that usually segment an object selected with a point or box—SAM 3 is designed to discover all instances matching a concept.
For example, you can ask for yellow school bus, striped red umbrella, or person. The output includes masks, bounding boxes, confidence scores, and instance identities. SAM 3.1, released on March 27, 2026, is the newer drop-in update, particularly improving multi-object video tracking.
What is Meta SAM 3?
SAM 3 extends the Segment Anything family from interactive object selection to open-vocabulary concept discovery. A point, box, or mask tells a model where an object is. A concept prompt tells SAM 3 what kind of object to find.
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Its target task is instance-level segmentation: the model attempts to return a separate mask and identity for every matching object, including the valid result that no matching object is present. “Every” describes the benchmark objective, not a guarantee of perfect recall in crowded, occluded, tiny, or visually ambiguous scenes.
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Meta’s research page describes SAM 3 as a unified detector-and-tracker system trained and evaluated with its Segment Anything with Concepts, or SA-Co, benchmark. The official research release is dated November 19, 2025, while Meta’s launch blog presents the public announcement on November 20, 2025. See Meta’s research page and launch announcement.
SAM 3 versus SAM 1, SAM 2, and SAM 3.1
| Model | Main prompts | Main strength | Typical output |
|---|---|---|---|
| SAM 1 | Points, boxes, masks | Interactive image segmentation | Object masks |
| SAM 2 | Visual prompts plus video memory | Image and video object tracking | Masks and tracked masklets |
| SAM 3 | Text, image exemplars, points, boxes, masks | Open-vocabulary concept segmentation | Masks, boxes, scores, and IDs |
| SAM 3.1 | SAM 3-compatible prompts | More efficient multi-object video tracking | Faster multi-object tracking |
SAM 3 is therefore not simply “SAM 2 with better masks.” The important change is concept-conditioned detection: it can search an image for matching instances rather than waiting for a human to identify each object spatially.
What is Promptable Concept Segmentation?
PCS supplies a short text phrase, image exemplar, or combination of prompts and returns masks plus unique identities for matching instances.
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- Object detection returns boxes and labels, generally from a predefined vocabulary.
- Semantic segmentation labels pixels by class but does not necessarily separate individual objects.
- Instance segmentation separates each object instance into its own mask.
- Referring-expression segmentation usually handles a particular object described with a longer phrase or spatial relationship.
- Interactive segmentation segments an object selected with a point, box, or mask.
SAM 3 combines open-vocabulary concept matching with instance masks and retains the visual-prompt workflows associated with earlier SAM models.
Which prompts does SAM 3 accept?
Short text phrases
Use concise noun phrases such as:
red apple
yellow school bus
person wearing a hatThe base model is designed for short concepts, not unrestricted natural-language reasoning. A request such as “the second-to-last book from the right on the top shelf” is too relational and specific to treat as a dependable direct prompt.
Image exemplars
An image crop or visual example can describe an unusual object, a domain-specific appearance, or a subtype that is difficult to name. This is useful when appearance matters more than a generic category label.
Combined prompts
Text can establish semantic intent while an exemplar constrains appearance. This can reduce ambiguity, but it does not guarantee that every visually similar object will be correctly accepted or rejected.
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Points, boxes, and masks
Visual prompts remain useful when a human already knows which object to segment. Choose these prompts when the task is interactive selection rather than exhaustive concept search.
How SAM 3 works
At a high level, the architecture contains a shared vision backbone, an image-level detector, and a memory-based video tracker. The detector can be conditioned on text, geometry, and image exemplars. A presence head helps separate the question “does this concept exist?” from the localization problem of finding it. The tracker is derived from the transformer encoder-decoder approach used in SAM 2.
The engineering challenge is balancing two competing needs: different objects matching one concept need sufficiently similar representations for discovery, while individual objects need distinct identities so they can be separated and tracked over time. The current repository describes a model of approximately 848 million parameters.
What can SAM 3 do?
- Find and mask every visible person in an image.
- Segment all red cars or yellow school buses without training a fixed class detector.
- Locate a visually unusual object from an image exemplar.
- Track matching animals, people, or vehicles through video.
- Provide masks and boxes to an annotation, editing, robotics, or measurement workflow.
- Return a negative result when a requested concept is not detected.
In production, treat the output as an input to a reviewable pipeline. Thresholds, duplicate removal, manual correction, and domain validation remain necessary.
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SAM 3.1 was released by Meta on March 27, 2026 as a drop-in replacement for SAM 3. Its main change is object multiplexing: up to 16 objects can be tracked in one forward pass instead of processing each object independently.
Meta reports that this can raise throughput from 16 to 32 frames per second on one H100 GPU for videos containing a medium number of objects, while reducing redundant computation and GPU memory pressure. These are Meta-reported results, not a universal performance guarantee. Video resolution, object count, prompt type, precision, implementation, and hardware all affect throughput.
The original SAM 3 remains important for understanding the release, but the current official repository includes SAM 3.1 checkpoints and instructions. Check the repository before copying older setup examples.
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How to install SAM 3 locally
Prerequisites checked August 18, 2026: Python 3.12 or newer, PyTorch 2.7 or newer, and a CUDA-compatible GPU with CUDA 12.6 or newer. Meta’s example installation uses PyTorch 2.10.0 with CUDA 12.8 wheels.
conda create -n sam3 python=3.12
conda deactivate
conda activate sam3
pip install torch==2.10.0 torchvision
--index-url https://download.pytorch.org/whl/cu128
git clone https://github.com/facebookresearch/sam3.git
cd sam3
pip install -e .For notebooks or development:
pip install -e "[notebooks]"
pip install -e "[train,dev]"Optional acceleration packages documented by the repository include:
pip install einops ninja
pip install flash-attn-3 --no-deps
--index-url https://download.pytorch.org/whl/cu128
pip install git+https://github.com/ronghanghu/cc_torch.gitThese commands are version-sensitive. If dependency resolution fails, use the latest instructions in the repository rather than mixing commands from older tutorials.
Requesting the checkpoints
The code is public, but the official SAM 3 checkpoints require access approval on Hugging Face. The practical flow is:
- Open the official model page and request access.
- After approval, create or use a Hugging Face access token.
- Authenticate locally:
hf auth loginThen load or download the approved checkpoint. Do not assume that a public GitHub repository means unrestricted weight downloads.
Run image inference
The native repository demonstrates this basic text-prompt path:
import torch
from PIL import Image
from sam3.model_builder import build_sam3_image_model
from sam3.model.sam3_image_processor import Sam3Processor
model = build_sam3_image_model()
processor = Sam3Processor(model)
image = Image.open("<YOUR_IMAGE_PATH.jpg>")
inference_state = processor.set_image(image)
output = processor.set_text_prompt(
state=inference_state,
prompt="yellow school bus",
)
masks = output["masks"]
boxes = output["boxes"]
scores = output["scores"]Use masks for pixel-level editing or measurement, boxes for downstream detection-style interfaces, and scores for filtering and review queues. Validate threshold choices on your own data; a score is not a calibrated guarantee of correctness.
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Run video inference
The native video API starts a session, adds a prompt on an initial frame, and returns tracked outputs:
from sam3.model_builder import build_sam3_video_predictor
video_predictor = build_sam3_video_predictor()
response = video_predictor.handle_request({
"type": "start_session",
"resource_path": "<YOUR_VIDEO_PATH>",
})
response = video_predictor.handle_request({
"type": "add_prompt",
"session_id": response["session_id"],
"frame_index": 0,
"text": "person",
})
output = response["outputs"]The repository supports an MP4 file or a folder of JPEG frames. A complete application should inspect propagated masks, confidence, duplicate tracks, missed objects, and identity switches rather than judging video quality from a single frame.
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Pre-loaded versus streaming video
The Hugging Face implementation supports pre-loaded and streaming sessions. Pre-loaded inference can use future frames to remove unmatched or duplicate tracks through hotstart heuristics. Streaming cannot use future frames, so it may produce more false positives or duplicate tracks. Use pre-loaded inference when the full clip is available; use streaming when live latency matters, then add application-side track filtering and confidence rules.
Use SAM 3 with Hugging Face Transformers
The official model page documents a Transformers pipeline:
from transformers import pipeline
pipe = pipeline(
"mask-generation",
model="facebook/sam3",
)You can also load the processor and model directly:
from transformers import AutoProcessor, AutoModel
processor = AutoProcessor.from_pretrained("facebook/sam3")
model = AutoModel.from_pretrained(
"facebook/sam3",
device_map="auto",
)This route is convenient for teams already using Transformers, notebooks, Colab, or Kaggle. It is not the same as a guaranteed managed production endpoint: the model page documents local use and does not show a SAM 3-specific inference-provider price.
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SA-Co is both a data-engineering initiative and an evaluation framework. Meta says it includes more than 4 million unique concept labels and covers image and video tasks, positive and negative prompts, instance masks, and unique IDs. The repository links image sets including SA-Co/Gold and SA-Co/Silver, along with the SA-Co/VEval video benchmark.
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Meta reports approximately a 2× gain over existing systems on its PCS image and video benchmarks, with comparisons involving systems such as OWLv2, GLEE, LLMDet, and Gemini 2.5 Pro. Meta also reports about 30 ms per image on an H200 GPU for a single image with more than 100 detected objects, near-real-time video for roughly five concurrent tracked objects in the original SAM 3 description, and an approximately three-to-one user preference advantage over OWLv2 in one study.
These figures are conditions-specific. Resolution, prompt formulation, object count, batch size, precision, implementation, and benchmark composition all matter. They should be read as Meta-reported results on Meta-defined evaluations, not proof that SAM 3 is best for every domain or deployment.
Limitations and failure modes
Short prompts are a design constraint
“Red car” is a suitable concept. Long relational or reasoning-heavy instructions are not the base model’s strength. For requests such as “the object used to control the horse,” use an application layer or multimodal model to convert the request into several short concepts, inspect results, and refine prompts. Meta describes this surrounding approach as SAM 3 Agent; it is not evidence that the base model directly understands arbitrary long descriptions.
Fine-grained and specialized imagery
Meta notes weaknesses on fine-grained and out-of-domain concepts, including examples such as “platelet.” Medical, scientific, industrial, microscopy, and pathology imagery require independent validation. Fine-tuning with annotated data may help, but a small sample does not guarantee production quality.
Occlusion, crowding, and tiny objects
Objects that overlap, occupy few pixels, have unusual viewpoints, or look similar can be missed, merged, split, or duplicated. Broad prompts such as plant, tool, or vehicle are also product-design problems: define the concept, show examples, expose confidence thresholds, and provide manual correction.
Video cost and tracking quality
In the original SAM 3 implementation, video cost scales approximately linearly with the number of tracked objects because objects are processed separately while sharing frame-level embeddings. SAM 3.1 improves this through multiplexing, but crowded scenes still need measurement under realistic object counts.
License and access
The repository uses Meta’s SAM License, not simply an unrestricted permissive license. The Hugging Face page labels the model license as “other.” Review the exact license before commercial deployment, redistribution, or offering a hosted service. Local software may have no per-call Meta fee, but GPU, storage, hosting, annotation, and compliance costs still apply.
Alternatives and complementary systems
- SAM 1 or SAM 2: Prefer these for simpler interactive segmentation, especially when a human can provide a point or box and concept discovery is unnecessary.
- Fixed-vocabulary detectors or specialist segmenters: Prefer them when classes are stable, latency and memory are tightly constrained, or the domain is regulated and highly specialized.
- OWLv2-style open-vocabulary detection: Useful when boxes and open-vocabulary recognition are sufficient; SAM 3 is aimed at concept discovery with pixel masks and identities.
- Multimodal model plus SAM 3: Use this for complex language, relationships, exclusions, or reasoning. The multimodal layer proposes short prompts; SAM 3 performs localization and segmentation.
- Hosted platforms: Roboflow positions its platform for annotation, fine-tuning, evaluation, and deployment around SAM 3. Its public pricing page listed a free plan with 15 credits per month, Core at $79 per month billed annually or $99 billed monthly, and custom Enterprise pricing on August 18, 2026. Confirm current prices, model support, deployment terms, and licensing before purchase.
- Ultralytics: Its documentation offers a separate Python/CLI integration for SAM 3 workflows. This can suit teams already using Ultralytics, but it is not interchangeable with Meta’s native repository; check version compatibility, weight handling, supported features, and licensing.
See Ultralytics’ SAM 3 documentation and Roboflow’s pricing page for those vendor-specific options.
Is SAM 3 right for you?
| Use case | Assessment |
|---|---|
| Researchers | Strong fit for studying open-vocabulary instance segmentation, provided GPU access and checkpoint approval are available. |
| Annotators | Strong fit when text or exemplars can accelerate mask creation, with human review for ambiguous labels. |
| Video editors | Useful for concept-based selection and tracking; SAM 3.1 is preferable for many simultaneous objects. |
| Robotics teams | Potentially useful for flexible visual queries, but latency, safety, lighting, occlusion, and domain performance need task-specific testing. |
| Scientific users | Do not assume zero-shot reliability. Test carefully and expect specialist training or fine-tuning for rare structures. |
| Production developers | Choose it when open-vocabulary masks justify the infrastructure and validation burden; review license and checkpoint terms first. |
| Edge-device developers | Often a poor initial fit because the official setup targets recent CUDA GPUs and a large model; consider a smaller specialist model or hosted inference. |
Practical deployment checklist
- Define the concept vocabulary and write representative short prompts.
- Collect positive and negative examples from the actual deployment domain.
- Measure recall, precision, mask quality, duplicate detections, and identity switches—not only a single confidence threshold.
- Use exemplar prompts for rare or visually specific objects.
- Prefer pre-loaded video inference when future frames are available and latency allows it.
- Add manual correction and an uncertainty review queue.
- Benchmark total cost per image, frame, or video minute, including GPU and storage.
- Read the SAM License and confirm whether the intended commercial or hosted use is permitted.
The Bottom Line
Bottom line: SAM 3 is best understood as an open-vocabulary, concept-driven extension of SAM: it can search for matching instances with short text or visual examples and return masks, boxes, scores, and identities. It is compelling for flexible image and video workflows, but SAM 3.1 is the better current choice for multi-object video, and neither version removes the need for domain validation, suitable CUDA hardware, checkpoint approval, and license review.
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