Practical overview
When it fits
Use it when you need a research codebase and model options for exploring promptable segmentation with smaller encoders, or when studying distillation and segmentation evaluation workflows.
Who it is for
- Computer-vision developers prototyping text-prompted image segmentation.
- Researchers exploring model compression and knowledge distillation for segmentation.
- Teams investigating compact segmentation models for edge-oriented applications.
Documented uses
- Generate image masks from a text prompt using an EfficientSAM3 model.
- Compare full lightweight-encoder models with SAM3-LiteText variants.
- Explore model distillation, fine-tuning and COCO evaluation materials.
- Investigate video object segmentation and tracking as a research direction.
Requirements & limits
The project is presented as a public GitHub repository with checkpoint links in its README. Installation requires Python 3.10+ and PyTorch 2.0+; CUDA 11.8+ is listed for GPU support.
- Full EfficientSAM3 variants replace both the vision and text encoders; SAM3-LiteText retains SAM3’s vision encoder and replaces only its text encoder.
- The listed full-model options range from 89.2M to 95.3M parameters; LiteText variants range from 550.0M to 631.3M.
- The documented quick start is for image segmentation. Video segmentation and tracking are project topics, but the supplied README portion does not give a corresponding video-use recipe.
- The project documents Python and PyTorch requirements and a CUDA prerequisite for GPU support, but does not specify operating-system or runtime support.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install the repository and its Stage 1 dependencies
Prepare the project environment using the installation steps in its README.
Before you start
- Python 3.10 or later
- PyTorch 2.0 or later
- CUDA 11.8 or later for GPU support
Steps
Clone the public GitHub repository.
Change into the repository directory.
Install the editable package with the Stage 1 extra using the README command.
Expected outcome
The repository’s documented editable installation command has been run for the Stage 1 extra.
Guide limitations
- The README lists CUDA for GPU support; it does not specify supported operating systems or runtimes.
- This is an installation recipe, not a verification that installation or model inference succeeds in a particular environment.
Use the documented text-prompt image segmentation example
Follow the repository’s example to request masks for a text prompt on one image.
Before you start
- An installed project environment
- An EfficientSAM3 checkpoint, such as the listed TV-M model checkpoint
- A readable image file
Steps
Build the EfficientSAM3 image model with the documented model builder and checkpoint parameters.
Open the image as RGB and initialize Sam3Processor with the model.
Set the image in the processor, then submit a text prompt such as “dog”.
Read the masks and scores from the returned state.
Expected outcome
The example returns segmentation masks and scores for the prompted image.
Guide limitations
- The README example assumes the checkpoint is available at the path provided to the model builder.
- This documents image segmentation; it is not a video tracking procedure or a compatibility guarantee.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- SimonZeng7108/efficientsam3 · Official homepage title · retained in its source language