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*This model was released on 2025-11-19 and added to Hugging Face Transformers on 2025-11-19.*

# SAM3 Tracker

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## Overview

SAM3 (Segment Anything Model 3) was introduced in [SAM 3: Segment Anything with Concepts](https://ai.meta.com/research/publications/sam-3-segment-anything-with-concepts/).

Sam3Tracker performs **Promptable Visual Segmentation (PVS)** on images. PVS takes interactive visual prompts (points, boxes, masks) or text inputs to segment a **specific object instance** per prompt. This is the task that SAM 1 and SAM 2 focused on, and SAM 3 improves upon it.

Sam3Tracker is an updated version of SAM2 (Segment Anything Model 2) that maintains the same API while providing improved performance and capabilities.

The abstract from the paper is the following:

*We present Segment Anything Model (SAM) 3, a unified model that detects, segments, and tracks objects in images and videos based on concept prompts, which we define as either short noun phrases (e.g., "yellow school bus"), image exemplars, or a combination of both. Promptable Concept Segmentation (PCS) takes such prompts and returns segmentation masks and unique identities for all matching object instances. To advance PCS, we build a scalable data engine that produces a high-quality dataset with 4M unique concept labels, including hard negatives, across images and videos. Our model consists of an image-level detector and a memory-based video tracker that share a single backbone. Recognition and localization are decoupled with a presence head, which boosts detection accuracy. SAM 3 doubles the accuracy of existing systems in both image and video PCS, and improves previous SAM capabilities on visual segmentation tasks. We open source SAM 3 along with our new Segment Anything with Concepts (SA-Co) benchmark for promptable concept segmentation.*

This model was contributed by [yonigozlan](https://huggingface.co/yonigozlan) and [ronghanghu](https://huggingface.co/ronghanghu).

## Usage example

### Automatic Mask Generation with Pipeline

Sam3Tracker can be used for automatic mask generation to segment all objects in an image using the `mask-generation` pipeline:

```python
>>> from transformers import pipeline

>>> generator = pipeline("mask-generation", model="facebook/sam3", device=0)
>>> image_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/truck.jpg"
>>> outputs = generator(image_url, points_per_batch=64)

>>> len(outputs["masks"])  # Number of masks generated
39
```

### Basic Image Segmentation

#### Single Point Click

You can segment objects by providing a single point click on the object you want to segment:

```python
>>> from transformers import Sam3TrackerProcessor, Sam3TrackerModel
from accelerate import Accelerator
>>> import torch
>>> from PIL import Image
>>> import requests

>>> device = Accelerator().device

>>> model = Sam3TrackerModel.from_pretrained("facebook/sam3").to(device)
>>> processor = Sam3TrackerProcessor.from_pretrained("facebook/sam3")

>>> image_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/truck.jpg"
>>> raw_image = Image.open(requests.get(image_url, stream=True).raw).convert("RGB")

>>> input_points = [[[[500, 375]]]]  # Single point click, 4 dimensions (image_dim, object_dim, point_per_object_dim, coordinates)
>>> input_labels = [[[1]]]  # 1 for positive click, 0 for negative click, 3 dimensions (image_dim, object_dim, point_label)

>>> inputs = processor(images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(model.device)

>>> with torch.no_grad():
...     outputs = model(**inputs)

>>> masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])[0]

>>> # The model outputs multiple mask predictions ranked by quality score
>>> print(f"Generated {masks.shape[1]} masks with shape {masks.shape}")
Generated 3 masks with shape torch.Size([1, 3, 1500, 2250])
```

#### Multiple Points for Refinement

You can provide multiple points to refine the segmentation:

```python
>>> # Add both positive and negative points to refine the mask
>>> input_points = [[[[500, 375], [1125, 625]]]]  # Multiple points for refinement
>>> input_labels = [[[1, 1]]]  # Both positive clicks

>>> inputs = processor(images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs)

>>> masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])[0]
```

#### Bounding Box Input

Sam3Tracker also supports bounding box inputs for segmentation:

```python
>>> # Define bounding box as [x_min, y_min, x_max, y_max]
>>> input_boxes = [[[75, 275, 1725, 850]]]

>>> inputs = processor(images=raw_image, input_boxes=input_boxes, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs)

>>> masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])[0]
```

#### Multiple Objects Segmentation

You can segment multiple objects simultaneously:

```python
>>> # Define points for two different objects
>>> input_points = [[[[500, 375]], [[650, 750]]]]  # Points for two objects in same image
>>> input_labels = [[[1], [1]]]  # Positive clicks for both objects

>>> inputs = processor(images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(model.device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> # Each object gets its own mask
>>> masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])[0]
>>> print(f"Generated masks for {masks.shape[0]} objects")
Generated masks for 2 objects
```

### Batch Inference

#### Batched Images

Process multiple images simultaneously for improved efficiency:

```python
>>> from transformers import Sam3TrackerProcessor, Sam3TrackerModel
from accelerate import Accelerator
>>> import torch
>>> from PIL import Image
>>> import requests

>>> device = Accelerator().device

>>> model = Sam3TrackerModel.from_pretrained("facebook/sam3").to(device)
>>> processor = Sam3TrackerProcessor.from_pretrained("facebook/sam3")

>>> # Load multiple images
>>> image_urls = [
...     "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/truck.jpg",
...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dog-sam.png"
... ]
>>> raw_images = [Image.open(requests.get(url, stream=True).raw).convert("RGB") for url in image_urls]

>>> # Single point per image
>>> input_points = [[[[500, 375]]], [[[770, 200]]]]  # One point for each image
>>> input_labels = [[[1]], [[1]]]  # Positive clicks for both images

>>> inputs = processor(images=raw_images, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(model.device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> # Post-process masks for each image
>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
>>> print(f"Processed {len(all_masks)} images, each with {all_masks[0].shape[0]} objects")
Processed 2 images, each with 1 objects
```

#### Batched Objects per Image

Segment multiple objects within each image using batch inference:

```python
>>> # Multiple objects per image - different numbers of objects per image
>>> input_points = [
...     [[[500, 375]], [[650, 750]]],  # Truck image: 2 objects
...     [[[770, 200]]]  # Dog image: 1 object
... ]
>>> input_labels = [
...     [[1], [1]],  # Truck image: positive clicks for both objects
...     [[1]]  # Dog image: positive click for the object
... ]

>>> inputs = processor(images=raw_images, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
```

#### Batched Images with Batched Objects and Multiple Points

Handle complex batch scenarios with multiple points per object:

```python
>>> # Add groceries image for more complex example
>>> groceries_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/groceries.jpg"
>>> groceries_image = Image.open(requests.get(groceries_url, stream=True).raw).convert("RGB")
>>> raw_images = [raw_images[0], groceries_image]  # Use truck and groceries images

>>> # Complex batching: multiple images, multiple objects, multiple points per object
>>> input_points = [
...     [[[500, 375]], [[650, 750]]],  # Truck image: 2 objects with 1 point each
...     [[[400, 300]], [[630, 300], [550, 300]]]  # Groceries image: obj1 has 1 point, obj2 has 2 points
... ]
>>> input_labels = [
...     [[1], [1]],  # Truck image: positive clicks
...     [[1], [1, 1]]  # Groceries image: positive clicks for refinement
... ]

>>> inputs = processor(images=raw_images, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
```

#### Batched Bounding Boxes

Process multiple images with bounding box inputs:

```python
>>> # Multiple bounding boxes per image (using truck and groceries images)
>>> input_boxes = [
...     [[75, 275, 1725, 850], [425, 600, 700, 875], [1375, 550, 1650, 800], [1240, 675, 1400, 750]],  # Truck image: 4 boxes
...     [[450, 170, 520, 350], [350, 190, 450, 350], [500, 170, 580, 350], [580, 170, 640, 350]]  # Groceries image: 4 boxes
... ]

>>> # Update images for this example
>>> raw_images = [raw_images[0], groceries_image]  # truck and groceries

>>> inputs = processor(images=raw_images, input_boxes=input_boxes, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
>>> print(f"Processed {len(input_boxes)} images with {len(input_boxes[0])} and {len(input_boxes[1])} boxes respectively")
Processed 2 images with 4 and 4 boxes respectively
```

### Using Previous Masks as Input

Sam3Tracker can use masks from previous predictions as input to refine segmentation:

```python
>>> # Get initial segmentation
>>> input_points = [[[[500, 375]]]]
>>> input_labels = [[[1]]]
>>> inputs = processor(images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs)

>>> # Use the best mask as input for refinement
>>> mask_input = outputs.pred_masks[:, :, torch.argmax(outputs.iou_scores.squeeze())]

>>> # Add additional points with the mask input
>>> new_input_points = [[[[500, 375], [450, 300]]]]
>>> new_input_labels = [[[1, 1]]]
>>> inputs = processor(
...     input_points=new_input_points,
...     input_labels=new_input_labels,
...     original_sizes=inputs["original_sizes"],
...     return_tensors="pt",
... ).to(device)

>>> with torch.no_grad():
...     refined_outputs = model(
...         **inputs,
...         input_masks=mask_input,
...         image_embeddings=outputs.image_embeddings,
...         multimask_output=False,
...     )
```

## Sam3TrackerConfig

[[autodoc]] Sam3TrackerConfig

## Sam3TrackerPromptEncoderConfig

[[autodoc]] Sam3TrackerPromptEncoderConfig

## Sam3TrackerMaskDecoderConfig

[[autodoc]] Sam3TrackerMaskDecoderConfig

## Sam3TrackerProcessor

[[autodoc]] Sam3TrackerProcessor
    - __call__
    - post_process_masks

## Sam3TrackerModel

[[autodoc]] Sam3TrackerModel
    - forward
    - get_image_features

## Sam3TrackerPreTrainedModel

[[autodoc]] Sam3TrackerPreTrainedModel
    - forward
