mobilesam-lite 0.1.0

Unofficial MobileSAMv2 and MobileSAM software package for lightweight Segment Anything and everything inference.

MobileSAM_lite

An unofficial Python package for MobileSAM and MobileSAMv2 runtime that adds support for lighter encoder models not available in the original implementation.

This package vendors the runtime code needed for inference:

• mobilesamv2
• tinyvit
• efficientvit
• ultralytics under mobilesam_lite/_vendor/ultralytics

It intentionally does not bundle model checkpoints. Download weights separately and pass the checkpoint path at runtime.

The optional mobilesamv2.promt_mobilesamv2 module now resolves its Ultralytics dependency from the vendored package in mobilesam_lite._vendor.ultralytics.

Install locally

pip install -e .

Build distributions

python -m build

This will generate wheel and source distributions under dist/.

Example

import torch

from mobilesam_lite.mobile_sam import SamPredictor, sam_model_registry

model = sam_model_registry["vit_t"]("./weight/mobile_sam.pt")
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
model.eval()

predictor = SamPredictor(model)

Verify an installed wheel

After installing the wheel into a clean environment, run:

python example_inference_mobilesam.py --checkpoint /path/to/mobile_sam.pt

You can also provide a real image:

python example_inference_mobilesam.py --checkpoint /path/to/mobile_sam.pt --image /path/to/image.jpg

The script prints the installed distribution version, the imported package path, and the output tensor shapes from one prediction call.

For the MobileSAMv2 decoder path, use:

python example_inference_mobilesamv2.py \
  --checkpoint /path/to/mobile_sam.pt \
  --prompt-decoder-checkpoint /path/to/Prompt_guided_Mask_Decoder.pt \
  --object-aware-model-checkpoint /path/to/ObjectAwareModel.pt

This script verifies the packaged MobileSAMv2 pipeline with ObjectAwareModel box proposals plus the prompt-guided decoder, and writes boxes.png, mask_union.png, mask_union_overlay.png, and mask_overlay.png into the chosen output directory.

Reference: Official MobileSAM repository

https://github.com/chaoningzhang/mobilesam