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cubert-gmbh/efficient-track-anything

sourceHugging Faceapache-2.0updated 18d agoView on Hugging Face

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EfficientTAM checkpoints (mirror)

Byte-identical mirror of `yunyangx/efficient-track-anything` at revision 9bdd8ab585b19ef95f9c9ed847ac9478301890b4 (code and licence: <https://github.com/yformer/EfficientTAM>) for the Cuvis.AI plugins. No fine-tuning, conversion or re-serialization: the files below are the upstream release as published, with the upstream licence text in LICENSE.

Why this mirror exists

Cuvis.AI provisions model weights once into a shared cache (download-model download <name> from cuvis-ai-core) and then runs its pipelines in an offline, token-free runtime that only reads that cache. Hosting the exact upstream files under the cubert-gmbh organisation makes that provisioning reproducible (commit-pinned and sha256-verified) and takes the per-user Hugging Face account and token out of the users' path.

Files and provenance

File	Upstream source	Upstream revision / id	sha256	Size
`efficienttam_s.pt`	`yunyangx/efficient-track-anything` / `efficienttam_s.pt`	`9bdd8ab585b19ef95f9c9ed847ac9478301890b4`	`2b572be30d9e96ee29c8d785fe157c6b079ede7d56fbc8a3671d4120e63c89cd`	136.4 MB
`efficienttam_ti.pt`	`yunyangx/efficient-track-anything` / `efficienttam_ti.pt`	`9bdd8ab585b19ef95f9c9ed847ac9478301890b4`	`acbb17b28cca1f860acee09c9ecb6efdb732080dc7a85a07292c31813175fa7d`	71.6 MB
`efficienttam_s_512x512.pt`	`yunyangx/efficient-track-anything` / `efficienttam_s_512x512.pt`	`9bdd8ab585b19ef95f9c9ed847ac9478301890b4`	`67b5840012737ed2c94a4cb8787c5c1b27b3a946045d2e602e65ef77230b6085`	136.4 MB
`efficienttam_ti_512x512.pt`	`yunyangx/efficient-track-anything` / `efficienttam_ti_512x512.pt`	`9bdd8ab585b19ef95f9c9ed847ac9478301890b4`	`7d4d652a465f0081391050932f45d8a66768ccc99c8ea393ce8a5927e83f3b9b`	71.6 MB
`LICENSE`	<https://raw.githubusercontent.com/yformer/EfficientTAM/main/LICENSE>	n/a (URL; sha256 pinned)	`c71d239df91726fc519c6eb72d318ec65820627232b2f796219e87dcf35d0ab4`	11.4 kB

Mirrored on 2026-09-04 by Cubert GmbH from the sources above. The sha256 values are the upstream values; tools/mirror_weights.py check in cuvis-ai-core re-verifies this repository against them and against the upstream licence text.

Licence

Apache License 2.0. The LICENSE file is the upstream licence text taken verbatim from <https://github.com/yformer/EfficientTAM> (the upstream Hugging Face repository carries the apache-2.0 tag but ships no LICENSE file) at the mirror date. Copyright remains with the upstream authors; Cubert GmbH claims no rights in these files.

Usage with Cuvis.AI

text

uv run download-model download efficienttam_s
uv run download-model download efficienttam_ti
uv run download-model download efficienttam_s_512x512
uv run download-model download efficienttam_ti_512x512

download-model (from cuvis-ai-core) provisions the file into the shared Hugging Face cache and verifies its sha256. From cuvis-ai-core 0.16.0 on, ModelWeights.resolve("<name>") returns the cached path and the cuvis-ai-rtsam2 nodes resolve their weights through it, so a pipeline needs no manual step once the weights are provisioned; earlier plugin releases still fetch from their original upstream sources.