EdgeTAM hf EU AI Act Compliance Profile

Model Details

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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:

>>> 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:

>>> 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, re

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