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Model Zoo and Licences

The seven models Potato can run, what each costs to install, which licence applies, and where it runs. Nothing is bundled and nothing downloads unasked.

Potato runs several vision models. None ships with the package and none is fetched behind your back. potato/model_zoo.py is one registry behind all of them, and each entry carries its task, its licence, whether that licence needs explicit acceptance, and the parameters the browser needs.

bash
potato download-models --list
potato download-models mobile_sam

What is available

ModelJobRunsSizeLicence
mobile_samClick or box to maskBrowser45 MBMIT export, Apache-2.0 upstream
grounding_dino_tinyText to boxesBrowser145 MBApache-2.0
sam2_video_tinyPrompt one frame, track the restServer181 MBApache-2.0
edge_samClick to mask, fastest on weak hardwareBrowser—Non-commercial only
sam3Text to boxes and masks in one modelServer~3.5 GBMeta SAM License
edgetamOn-device tracking——Apache-2.0
onnxruntimeThe inference runtime the others needBrowser13.5 MBMIT

Two entries have no download URL. edge_sam permits non-commercial use only, so making it one command away would invite installing it without reading the licence. edgetam has no published ONNX export at all, since upstream ships CoreML, so there is nothing to fetch yet.

Licence gating is enforced, not documented

download-models --list prints <-- NON-COMMERCIAL next to any model whose licence needs it, and download-models refuses to fetch one without --accept-licence. No weights for those models ship in the package.

For a research audience this is the point worth making: a tool that tells you a model is non-commercial before you build a dataset with it is doing something the industry mostly does not. Discovering the licence after collection is discovering it too late.

Air-gapped installs

Run the downloads on a connected machine and copy the model directory across. The application itself needs no network — every stylesheet, script, font and icon serves from the install, verified at 62 requests, zero external on a rendered annotation page.

The accurate phrasing is "air-gapped once the weights are on the machine". The application needs no network; the models are a one-time transfer; and any hosted AI endpoint you configure obviously needs a route out. See air-gapped deployment.