Repository for SAM 2: Segment Anything in Images and Videos, a foundation model towards solving promptable visual segmentation in images and videos from FAIR. See the SAM 2 paper for more information.
The official code is publicly release in this repo .
Usage#
For image prediction:
import torch
from sam2.sam2_image_predictor import SAM2ImagePredictor
predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2.1-hiera-small")
with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
predictor.set_image(<your_image>)
masks, _, _ = predictor.predict(<input_prompts>)For video prediction:
import torch
from sam2.sam2_video_predictor import SAM2VideoPredictor
predictor = SAM2VideoPredictor.from_pretrained("facebook/sam2.1-hiera-small")
with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
state = predictor.init_state(<your_video>)
# add new prompts and instantly get the output on the same frame
frame_idx, object_ids, masks = predictor.add_new_points_or_box(state, <your_prompts>):
# propagate the prompts to get masklets throughout the video
for frame_idx, object_ids, masks in predictor.propagate_in_video(state):
...Refer to the demo notebooks for details.
Automatic Mask Generation with Pipeline#
SAM2 can be used for automatic mask generation to segment all objects in an image using the mask-generation pipeline:
>>> from transformers import pipeline
>>> generator = pipeline("mask-generation", model="facebook/sam2.1-hiera-small", 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
39Basic Image Segmentation#
Single Point Click#
You can segment objects by providing a single point click on the object you want to segment:
>>> from transformers import Sam2Processor, Sam2Model
>>> import torch
>>> from PIL import Image
>>> import requests
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> model = Sam2Model.from_pretrained("facebook/sam2.1-hiera-small").to(device)
>>> processor = Sam2Processor.from_pretrained("facebook/sam2.1-hiera-small")
>>> 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(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:
>>> # 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]SAM2 also supports bounding box inputs for segmentation:
>>> # 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:
>>> # 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(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 objectsBatch Inference#
Batched Images#
Process multiple images simultaneously for improved efficiency:
>>> from transformers import Sam2Processor, Sam2Model
>>> import torch
>>> from PIL import Image
>>> import requests
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> model = Sam2Model.from_pretrained("facebook/sam2.1-hiera-small").to(device)
>>> processor = Sam2Processor.from_pretrained("facebook/sam2.1-hiera-small")
>>> # 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(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 objectsBatched Objects per Image#
Segment multiple objects within each image using batch inference:
>>> # 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:
>>> # 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:
>>> # 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 respectivelySAM2 can use masks from previous predictions as input to refine segmentation:
>>> # 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,
... )Video Segmentation and Tracking#
SAM2's key strength is its ability to track objects across video frames. Here's how to use it for video segmentation:
Basic Video Tracking#
>>> from transformers import Sam2VideoModel, Sam2VideoProcessor
>>> import torch
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> model = Sam2VideoModel.from_pretrained("facebook/sam2.1-hiera-small").to(device, dtype=torch.bfloat16)
>>> processor = Sam2VideoProcessor.from_pretrained("facebook/sam2.1-hiera-small")
>>> # Load video frames (example assumes you have a list of PIL Images)
>>> # video_frames = [Image.open(f"frame_{i:05d}.jpg") for i in range(num_frames)]
>>> # For this example, we'll use the video loading utility
>>> from transformers.video_utils import load_video
>>> video_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/bedroom.mp4"
>>> video_frames, _ = load_video(video_url)
>>> # Initialize video inference session
>>> inference_session = processor.init_video_session(
... video=video_frames,
... inference_device=device,
... torch_dtype=torch.bfloat16,
... )
>>> # Add click on first frame to select object
>>> ann_frame_idx = 0
>>> ann_obj_id = 1
>>> points = [[[[210, 350]]]]
>>> labels = [[[1]]]
>>> processor.add_inputs_to_inference_session(
... inference_session=inference_session,
... frame_idx=ann_frame_idx,
... obj_ids=ann_obj_id,
... input_points=points,
... input_labels=labels,
... )
>>> # Segment the object on the first frame
>>> outputs = model(
... inference_session=inference_session,
... frame_idx=ann_frame_idx,
... )
>>> video_res_masks = processor.post_process_masks(
... [outputs.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False
... )[0]
>>> print(f"Segmentation shape: {video_res_masks.shape}")
Segmentation shape: torch.Size([1, 1, 480, 854])
>>> # Propagate through the entire video
>>> video_segments = {}
>>> for sam2_video_output in model.propagate_in_video_iterator(inference_session):
... video_res_masks = processor.post_process_masks(
... [sam2_video_output.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False
... )[0]
... video_segments[sam2_video_output.frame_idx] = video_res_masks
>>> print(f"Tracked object through {len(video_segments)} frames")
Tracked object through 180 framesMulti-Object Video Tracking#
Track multiple objects simultaneously across video frames:
>>> # Reset for new tracking session
>>> inference_session.reset_inference_session()
>>> # Add multiple objects on the first frame
>>> ann_frame_idx = 0
>>> obj_ids = [2, 3]
>>> input_points = [[[[200, 300]], [[400, 150]]]] # Points for two objects (batched)
>>> input_labels = [[[1], [1]]]
>>> processor.add_inputs_to_inference_session(
... inference_session=inference_session,
... frame_idx=ann_frame_idx,
... obj_ids=obj_ids,
... input_points=input_points,
... input_labels=input_labels,
... )
>>> # Get masks for both objects on first frame
>>> outputs = model(
... inference_session=inference_session,
... frame_idx=ann_frame_idx,
... )
>>> # Propagate both objects through video
>>> video_segments = {}
>>> for sam2_video_output in model.propagate_in_video_iterator(inference_session):
... video_res_masks = processor.post_process_masks(
... [sam2_video_output.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False
... )[0]
... video_segments[sam2_video_output.frame_idx] = {
... obj_id: video_res_masks[i]
... for i, obj_id in enumerate(inference_session.obj_ids)
... }
>>> print(f"Tracked {len(inference_session.obj_ids)} objects through {len(video_segments)} frames")
Tracked 2 objects through 180 framesRefining Video Segmentation#
You can add additional clicks on any frame to refine the tracking:
>>> # Add refinement click on a later frame
>>> refine_frame_idx = 50
>>> ann_obj_id = 2 # Refining first object
>>> points = [[[[220, 280]]]] # Additional point
>>> labels = [[[1]]] # Positive click
>>> processor.add_inputs_to_inference_session(
... inference_session=inference_session,
... frame_idx=refine_frame_idx,
... obj_ids=ann_obj_id,
... input_points=points,
... input_labels=labels,
... )
>>> # Re-propagate with the additional information
>>> video_segments = {}
>>> for sam2_video_output in model.propagate_in_video_iterator(inference_session):
... video_res_masks = processor.post_process_masks(
... [sam2_video_output.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False
... )[0]
... video_segments[sam2_video_output.frame_idx] = video_res_masksStreaming Video Inference#
For real-time applications, SAM2 supports processing video frames as they arrive:
>>> # Initialize session for streaming
>>> inference_session = processor.init_video_session(
... inference_device=device,
... torch_dtype=torch.bfloat16,
... )
>>> # Process frames one by one
>>> for frame_idx, frame in enumerate(video_frames[:10]): # Process first 10 frames
... inputs = processor(images=frame, device=device, return_tensors="pt")
...
... if frame_idx == 0:
... # Add point input on first frame
... processor.add_inputs_to_inference_session(
... inference_session=inference_session,
... frame_idx=0,
... obj_ids=1,
... input_points=[[[[210, 350], [250, 220]]]],
... input_labels=[[[1, 1]]],
... original_size=inputs.original_sizes[0], # need to be provided when using streaming video inference
... )
...
... # Process current frame
... sam2_video_output = model(inference_session=inference_session, frame=inputs.pixel_values[0])
...
... video_res_masks = processor.post_process_masks(
... [sam2_video_output.pred_masks], original_sizes=inputs.original_sizes, binarize=False
... )[0]
... print(f"Frame {frame_idx}: mask shape {video_res_masks.shape}")Video Batch Processing for Multiple Objects#
Track multiple objects simultaneously in video by adding them all at once:
>>> # Initialize video session
>>> inference_session = processor.init_video_session(
... video=video_frames,
... inference_device=device,
... torch_dtype=torch.bfloat16,
... )
>>> # Add multiple objects on the first frame using batch processing
>>> ann_frame_idx = 0
>>> obj_ids = [2, 3] # Track two different objects
>>> input_points = [
... [[[200, 300], [230, 250], [275, 175]], [[400, 150]]]
... ] # Object 2: 3 points (2 positive, 1 negative); Object 3: 1 point
>>> input_labels = [
... [[1, 1, 0], [1]]
... ] # Object 2: positive, positive, negative; Object 3: positive
>>> processor.add_inputs_to_inference_session(
... inference_session=inference_session,
... frame_idx=ann_frame_idx,
... obj_ids=obj_ids,
... input_points=input_points,
... input_labels=input_labels,
... )
>>> # Get masks for all objects on the first frame
>>> outputs = model(
... inference_session=inference_session,
... frame_idx=ann_frame_idx,
... )
>>> video_res_masks = processor.post_process_masks(
... [outputs.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False
... )[0]
>>> print(f"Generated masks for {video_res_masks.shape[0]} objects")
Generated masks for 2 objects
>>> # Propagate all objects through the video
>>> video_segments = {}
>>> for sam2_video_output in model.propagate_in_video_iterator(inference_session):
... video_res_masks = processor.post_process_masks(
... [sam2_video_output.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False
... )[0]
... video_segments[sam2_video_output.frame_idx] = {
... obj_id: video_res_masks[i]
... for i, obj_id in enumerate(inference_session.obj_ids)
... }
>>> print(f"Tracked {len(inference_session.obj_ids)} objects through {len(video_segments)} frames")
Tracked 2 objects through 180 framesCitation#
To cite the paper, model, or software, please use the below:
@article{ravi2024sam2,
title={SAM 2: Segment Anything in Images and Videos},
author={Ravi, Nikhila and Gabeur, Valentin and Hu, Yuan-Ting and Hu, Ronghang and Ryali, Chaitanya and Ma, Tengyu and Khedr, Haitham and R{\"a}dle, Roman and Rolland, Chloe and Gustafson, Laura and Mintun, Eric and Pan, Junting and Alwala, Kalyan Vasudev and Carion, Nicolas and Wu, Chao-Yuan and Girshick, Ross and Doll{\'a}r, Piotr and Feichtenhofer, Christoph},
journal={arXiv preprint arXiv:2408.00714},
url={https://arxiv.org/abs/2408.00714},
year={2024}
}
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