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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:
pip install osam
For osam serve
:
pip install osam[serve]
To run with EfficientSAM:
osam run efficientsam --image <image_file>
To run with YOLO-World:
osam run yoloworld --image <image_file>
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 |
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
.
# 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
osam run efficientsam --image examples/_images/dogs.jpg \
--prompt '{"points": [[1439, 504], [1439, 1289]], "point_labels": [1, 1]}' \
> efficientsam.png
osam run yoloworld --image examples/_images/dogs.jpg --prompt '{"texts": ["dog"]}' \
> yoloworld.png
Input and output images ('dogs.jpg', 'efficientsam.png', 'yoloworld.png').
import osam.apis
import osam.types
request = osam.types.GenerateRequest(
model="efficientsam",
image=np.asarray(PIL.Image.open("examples/_images/dogs.jpg")),
prompt=osam.types.Prompt(points=[[1439, 504], [1439, 1289]], point_labels=[1, 1]),
)
response = osam.apis.generate(request=request)
PIL.Image.fromarray(response.mask).save("mask.png")
Input and output images ('dogs.jpg', 'mask.png').
# pip install osam[serve] # required for `osam serve`
# Get up the server
osam serve
# POST request
curl 127.0.0.1:11368/api/generate -X POST \
-H "Content-Type: application/json" \
-d "{\"model\": \"efficientsam\", \"image\": \"$(cat examples/_images/dogs.jpg | base64)\"}" \
| jq -r .mask | base64 --decode > mask.png
FAQs
Get up and running vision foundational models locally.
We found that osam demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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