Osam

Get up and running with promptable vision models locally.

Osam (/oʊˈsɑm/) is a tool to run open-source promptable vision models locally (inspired by Ollama).

Osam provides:

• Promptable Vision Models - Segment Anything Model (SAM), EfficientSAM, YOLO-World;
• Local APIs - CLI & Python & HTTP interface;
• Customization - Host custom vision models.

Installation

Pip

pip install osam

For osam serve:

pip install osam[serve]

Quickstart

To run with EfficientSAM:

osam run efficientsam --image <image_file>

To run with YOLO-World:

osam run yoloworld --image <image_file>

Model library

Here are models that can be downloaded:

Model	Parameters	Size	Download
SAM 100M	94M	100MB	osam run sam:100m
SAM 300M	313M	310MB	osam run sam:300m
SAM 600M	642M	630MB	osam run sam
SAM2 Tiny	39M	150MB	osam run sam2:tiny
SAM2 Small	46M	170MB	osam run sam2:small
SAM2 BasePlus	82M	300MB	osam run sam2
SAM2 Large	227M	870MB	osam run sam2:large
SAM3	893M	3.4GB	osam run sam3
EfficientSAM 10M	10M	40MB	osam run efficientsam:10m
EfficientSAM 30M	26M	100MB	osam run efficientsam
YOLO-World XL	168M	640MB	osam run yoloworld

PS. sam, efficientsam is equivalent to sam:latest, efficientsam:latest.

Custom download endpoint

Models are downloaded from their canonical URLs by default. To download from a mirror instead (for example, in networks where the canonical hosts are blocked), set OSAM_BLOB_ENDPOINT to a comma-separated list of endpoints tried in order:

# Try the mirror first, then fall back to the canonical host.
export OSAM_BLOB_ENDPOINT="https://mirror.example.com,direct"

Each blob is fetched from <endpoint>/<sha256-hex>; the reserved value direct uses the model's canonical URL. Downloads are verified by SHA-256 regardless of the endpoint, so a mirror cannot serve corrupted data. When unset, only direct is used. Omit direct from the list (for example OSAM_BLOB_ENDPOINT="https://mirror.example.com") to disable the canonical fallback and serve every blob from the mirror only.

Usage

CLI

# Run a model with an image
osam run efficientsam --image examples/_images/dogs.jpg > output.png

# Get a JSON output
osam run efficientsam --image examples/_images/dogs.jpg --json
# {"model": "efficientsam", "mask": "..."}

# Give a prompt
# 1. point prompt (background=0, foreground=1)
osam run efficientsam --image examples/_images/dogs.jpg \
  --prompt '{"points": [[1439, 504], [1439, 1289]], "point_labels": [1, 1]}' \
  > efficientsam_point.png
# 2. box prompt with sam2 (lt=2, rb=3)
osam run sam2 --image examples/_images/dogs.jpg \
  --prompt '{"points": [[1233, 376], [1649, 691]], "point_labels": [2, 3]}' \
  > sam2_box.png
# 3. text prompt
osam run sam3 --image examples/_images/dogs.jpg --prompt '{"texts": ["dog"]}' \
  > sam3_text.png
# 4. box prompt with sam3 (lt=2, rb=3)
osam run sam3 --image examples/_images/dogs.jpg \
  --prompt '{"points": [[1233, 376], [1649, 691]], "point_labels": [2, 3]}' \
  > sam3_box.png

Output images: 'efficientsam_point.png', 'sam2_box.png', 'sam3_text.png', 'sam3_box.png'

Python

import numpy as np
from PIL import Image

import osam

image = np.asarray(Image.open("examples/_images/dogs.jpg"))
request = osam.types.GenerateRequest(
    model="efficientsam",
    image=image,
    prompt=osam.types.Prompt(points=[[1439, 504], [1439, 1289]], point_labels=[1, 1]),
)
response = osam.apis.generate(request=request)
annotation = response.annotations[0]
mask = np.zeros(image.shape[:2], dtype=np.bool_)
bbox = annotation.bounding_box
mask[bbox.ymin:bbox.ymax + 1, bbox.xmin:bbox.xmax + 1] = annotation.mask
Image.fromarray(mask).save("mask.png")

Input and output images ('dogs.jpg', 'mask.png').

HTTP

# pip install osam[serve]  # required for `osam serve`

# Get up the server
osam serve

# POST request
base64 < examples/_images/dogs.jpg \
  | jq -Rs '{model: "efficientsam", image: gsub("\n"; "")}' \
  | curl 127.0.0.1:11368/api/generate -X POST --json @- \
  | jq -r '.annotations[0].mask' | base64 --decode > mask.png