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yonigozlan/
EdgeTAM-hf

Name: EdgeTAM-hf
Rating: 2.5 (35184 reviews)

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Model Details

[📃 Tech Report] [📂 Github] [🤗 Demo]

EdgeTAM is an on-device executable variant of the SAM 2 for promptable segmentation and tracking in videos. It runs 22× faster than SAM 2 and achieves 16 FPS on iPhone 15 Pro Max without quantization.

How to use with Transformers

Automatic Mask Generation with Pipeline

EdgeTAM 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="yonigozlan/EdgeTAM-hf", 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 Sam2Processor, EdgeTamModel
>>> import torch
>>> from PIL import Image
>>> import requests

>>> device = "cuda" if torch.cuda.is_available() else "cpu"

>>> model = EdgeTamModel.from_pretrained("yonigozlan/EdgeTAM-hf").to(device)
>>> processor = Sam2Processor.from_pretrained("yonigozlan/EdgeTAM-hf")

>>> 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:

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

EdgeTAM 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(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 Sam2Processor, EdgeTamModel
>>> import torch
>>> from PIL import Image
>>> import requests

>>> device = "cuda" if torch.cuda.is_available() else "cpu"

>>> model = EdgeTamModel.from_pretrained("yonigozlan/EdgeTAM-hf").to(device)
>>> processor = Sam2Processor.from_pretrained("yonigozlan/EdgeTAM-hf")

>>> # 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 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

EdgeTAM 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,
...     )

Video Segmentation and Tracking

EdgeTAM's key strength is its ability to track objects across video frames. Here's how to use it for video segmentation:

Basic Video Tracking

python

>>> from transformers import EdgeTamVideoModel, Sam2VideoProcessor
>>> import torch

>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> model = EdgeTamVideoModel.from_pretrained("yonigozlan/EdgeTAM-hf").to(device, dtype=torch.bfloat16)
>>> processor = Sam2VideoProcessor.from_pretrained("yonigozlan/EdgeTAM-hf")

>>> # 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 edgetam_video_output in model.propagate_in_video_iterator(inference_session):
...     video_res_masks = processor.post_process_masks(
...         [edgetam_video_output.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False
...     )[0]
...     video_segments[edgetam_video_output.frame_idx] = video_res_masks

>>> print(f"Tracked object through {len(video_segments)} frames")
Tracked object through 180 frames

Multi-Object Video Tracking

Track multiple objects simultaneously across video frames:

python

>>> # 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 edgetam_video_output in model.propagate_in_video_iterator(inference_session):
...     video_res_masks = processor.post_process_masks(
...         [edgetam_video_output.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False
...     )[0]
...     video_segments[edgetam_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 frames

Refining Video Segmentation

You can add additional clicks on any frame to refine the tracking:

python

>>> # 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 edgetam_video_output in model.propagate_in_video_iterator(inference_session):
...     video_res_masks = processor.post_process_masks(
...         [edgetam_video_output.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False
...     )[0]
...     video_segments[edgetam_video_output.frame_idx] = video_res_masks

Streaming Video Inference

For real-time applications, EdgeTAM supports processing video frames as they arrive:

python

>>> # 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
...     edgetam_video_output = model(inference_session=inference_session, frame=inputs.pixel_values[0])
...
...     video_res_masks = processor.post_process_masks(
...         [edgetam_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:

python

>>> # 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 edgetam_video_output in model.propagate_in_video_iterator(inference_session):
...     video_res_masks = processor.post_process_masks(
...         [edgetam_video_output.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False
...     )[0]
...     video_segments[edgetam_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 frames

Citation

If you find our code useful for your research, please consider citing:

@article{zhou2025edgetam,
  title={EdgeTAM: On-Device Track Anything Model},
  author={Zhou, Chong and Zhu, Chenchen and Xiong, Yunyang and Suri, Saksham and Xiao, Fanyi and Wu, Lemeng and Krishnamoorthi, Raghuraman and Dai, Bo and Loy, Chen Change and Chandra, Vikas and Soran, Bilge},
  journal={arXiv preprint arXiv:2501.07256},
  year={2025}
}