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Efficient Track Anything

[📕Project][🤗Gradio Demo][📕Paper][🤗Checkpoints]

The Efficient Track Anything Model(EfficientTAM) takes a vanilla lightweight ViT image encoder. An efficient memory cross-attention is proposed to further improve the efficiency. Our EfficientTAMs are trained on SA-1B (image) and SA-V (video) datasets. EfficientTAM achieves comparable performance with SAM 2 with improved efficiency. Our EfficientTAM can run >10 frames per second with reasonable video segmentation performance on iPhone 15. Try our demo with a family of EfficientTAMs at [🤗Gradio Demo].

Efficient Track Anything design

News

[Jan.5 2025] We add the support for running Efficient Track Anything on Macs with MPS backend. Check the example app.py.

[Jan.3 2025] We update the codebase of Efficient Track Anything, adpated from the latest SAM2 codebase with improved inference efficiency. Check the latest SAM2 update on Dec. 11 2024 for details. Thanks to SAM 2 team!

Efficient Track Anything Speed Update

[Dec.22 2024] We release 🤗Efficient Track Anything Checkpoints.

[Dec.4 2024] 🤗Efficient Track Anything for segment everything. Thanks to @SkalskiP!

[Dec.2 2024] We provide the preliminary version of Efficient Track Anything for demonstration.

Online Demo & Examples

Online demo and examples can be found in the project page.

EfficientTAM Video Segmentation Examples

SAM 2SAM2
EfficientTAMEfficientTAM

EfficientTAM Image Segmentation Examples

Input Image, SAM, EficientSAM, SAM 2, EfficientTAM

Point-promptpoint-prompt
Box-promptbox-prompt
Segment everythingsegment everything

Model

EfficientTAM checkpoints are available at the Hugging Face Space.

Getting Started

Installation

git clone https://github.com/yformer/EfficientTAM.git
cd EfficientTAM
conda create -n efficient_track_anything python=3.12
conda activate efficient_track_anything
pip install -e .

Download Checkpoints

cd checkpoints
./download_checkpoints.sh

We can benchmark FPS of efficient track anything models on GPUs and model size.

FPS Benchmarking and Model Size

cd ..
python efficient_track_anything/benchmark.py

Launching Gradio Demo Locally

For efficient track anything video, run

python app.py

For efficient track anything image, run

python app_image.py

Building Efficient Track Anything

You can build efficient track anything model with a config and initial the model with a checkpoint,

importtorchfromefficient_track_anything.build_efficienttamimport (
build_efficienttam_video_predictor,
)
checkpoint="./checkpoints/efficienttam_s.pt"model_cfg="configs/efficienttam/efficienttam_s.yaml"predictor=build_efficienttam_video_predictor(model_cfg, checkpoint)

Efficient Track Anything Notebook Example

The notebook is shared here

License

Efficient track anything checkpoints and codebase are licensed under Apache 2.0.

Acknowledgement

If you're using Efficient Track Anything in your research or applications, please cite using this BibTeX:

@article{xiong2024efficienttam,
title={Efficient Track Anything},
author={Yunyang Xiong, Chong Zhou, Xiaoyu Xiang, Lemeng Wu, Chenchen Zhu, Zechun Liu, Saksham Suri, Balakrishnan Varadarajan, Ramya Akula, Forrest Iandola, Raghuraman Krishnamoorthi, Bilge Soran, Vikas Chandra},
journal={preprint arXiv:2411.18933},
year={2024}
}

About

Efficient Track Anything

Resources

Readme
Apache-2.0 license
Activity

Stars

823 stars

Watchers

15 watching

Forks

44 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Efficient Track Anything
[📕Project][🤗Gradio Demo][📕Paper][🤗Checkpoints]
The Efficient Track Anything Model(EfficientTAM) takes a vanilla lightweight ViT image encoder. An efficient memory cross-attention is proposed to further improve the efficiency. Our EfficientTAMs are trained on SA-1B (image) and SA-V (video) datasets. EfficientTAM achieves comparable performance with SAM 2 with improved efficiency. Our EfficientTAM can run >10 frames per second with reasonable video segmentation performance on iPhone 15. Try our demo with a family of EfficientTAMs at [🤗Gradio Demo].
Efficient Track Anything design
News
[Jan.5 2025] We add the support for running Efficient Track Anything on Macs with MPS backend. Check the example app.py.
[Jan.3 2025] We update the codebase of Efficient Track Anything, adpated from the latest SAM2 codebase with improved inference efficiency. Check the latest SAM2 update on Dec. 11 2024 for details. Thanks to SAM 2 team!
Efficient Track Anything Speed Update
[Dec.22 2024] We release 🤗Efficient Track Anything Checkpoints.
[Dec.4 2024] 🤗Efficient Track Anything for segment everything. Thanks to @SkalskiP!
[Dec.2 2024] We provide the preliminary version of Efficient Track Anything for demonstration.
Online Demo & Examples
Online demo and examples can be found in the project page.
EfficientTAM Video Segmentation Examples
SAM 2SAM2
EfficientTAMEfficientTAM
EfficientTAM Image Segmentation Examples
Input Image, SAM, EficientSAM, SAM 2, EfficientTAM
Point-promptpoint-prompt
Box-promptbox-prompt
Segment everythingsegment everything
Model
EfficientTAM checkpoints are available at the Hugging Face Space.
Getting Started
Installation
git clone https://github.com/yformer/EfficientTAM.git
cd EfficientTAM
conda create -n efficient_track_anything python=3.12
conda activate efficient_track_anything
pip install -e .
Download Checkpoints
cd checkpoints
./download_checkpoints.sh
We can benchmark FPS of efficient track anything models on GPUs and model size.
FPS Benchmarking and Model Size
cd ..
python efficient_track_anything/benchmark.py
Launching Gradio Demo Locally
For efficient track anything video, run
python app.py

For efficient track anything image, run
python app_image.py

Building Efficient Track Anything
You can build efficient track anything model with a config and initial the model with a checkpoint,
importtorchfromefficient_track_anything.build_efficienttamimport (
build_efficienttam_video_predictor,
)
checkpoint="./checkpoints/efficienttam_s.pt"model_cfg="configs/efficienttam/efficienttam_s.yaml"predictor=build_efficienttam_video_predictor(model_cfg, checkpoint)
Efficient Track Anything Notebook Example
The notebook is shared here
License
Efficient track anything checkpoints and codebase are licensed under Apache 2.0.
Acknowledgement
If you're using Efficient Track Anything in your research or applications, please cite using this BibTeX:
@article{xiong2024efficienttam,
title={Efficient Track Anything},
author={Yunyang Xiong, Chong Zhou, Xiaoyu Xiang, Lemeng Wu, Chenchen Zhu, Zechun Liu, Saksham Suri, Balakrishnan Varadarajan, Ramya Akula, Forrest Iandola, Raghuraman Krishnamoorthi, Bilge Soran, Vikas Chandra},
journal={preprint arXiv:2411.18933},
year={2024}
}
About
Efficient Track Anything
Resources
Readme
Apache-2.0 license
Activity
Stars
823 stars
Watchers
15 watching
Forks
44 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Efficient Track Anything
[📕Project][🤗Gradio Demo][📕Paper][🤗Checkpoints]
The Efficient Track Anything Model(EfficientTAM) takes a vanilla lightweight ViT image encoder. An efficient memory cross-attention is proposed to further improve the efficiency. Our EfficientTAMs are trained on SA-1B (image) and SA-V (video) datasets. EfficientTAM achieves comparable performance with SAM 2 with improved efficiency. Our EfficientTAM can run >10 frames per second with reasonable video segmentation performance on iPhone 15. Try our demo with a family of EfficientTAMs at [🤗Gradio Demo].
Efficient Track Anything design
News
[Jan.5 2025] We add the support for running Efficient Track Anything on Macs with MPS backend. Check the example app.py.
[Jan.3 2025] We update the codebase of Efficient Track Anything, adpated from the latest SAM2 codebase with improved inference efficiency. Check the latest SAM2 update on Dec. 11 2024 for details. Thanks to SAM 2 team!
Efficient Track Anything Speed Update
[Dec.22 2024] We release 🤗Efficient Track Anything Checkpoints.
[Dec.4 2024] 🤗Efficient Track Anything for segment everything. Thanks to @SkalskiP!
[Dec.2 2024] We provide the preliminary version of Efficient Track Anything for demonstration.
Online Demo & Examples
Online demo and examples can be found in the project page.
EfficientTAM Video Segmentation Examples
SAM 2SAM2
EfficientTAMEfficientTAM
EfficientTAM Image Segmentation Examples
Input Image, SAM, EficientSAM, SAM 2, EfficientTAM
Point-promptpoint-prompt
Box-promptbox-prompt
Segment everythingsegment everything
Model
EfficientTAM checkpoints are available at the Hugging Face Space.
Getting Started
Installation
git clone https://github.com/yformer/EfficientTAM.git
cd EfficientTAM
conda create -n efficient_track_anything python=3.12
conda activate efficient_track_anything
pip install -e .
Download Checkpoints
cd checkpoints
./download_checkpoints.sh
We can benchmark FPS of efficient track anything models on GPUs and model size.
FPS Benchmarking and Model Size
cd ..
python efficient_track_anything/benchmark.py
Launching Gradio Demo Locally
For efficient track anything video, run
python app.py

For efficient track anything image, run
python app_image.py

Building Efficient Track Anything
You can build efficient track anything model with a config and initial the model with a checkpoint,
importtorchfromefficient_track_anything.build_efficienttamimport (
build_efficienttam_video_predictor,
)
checkpoint="./checkpoints/efficienttam_s.pt"model_cfg="configs/efficienttam/efficienttam_s.yaml"predictor=build_efficienttam_video_predictor(model_cfg, checkpoint)
Efficient Track Anything Notebook Example
The notebook is shared here
License
Efficient track anything checkpoints and codebase are licensed under Apache 2.0.
Acknowledgement
If you're using Efficient Track Anything in your research or applications, please cite using this BibTeX:
@article{xiong2024efficienttam,
title={Efficient Track Anything},
author={Yunyang Xiong, Chong Zhou, Xiaoyu Xiang, Lemeng Wu, Chenchen Zhu, Zechun Liu, Saksham Suri, Balakrishnan Varadarajan, Ramya Akula, Forrest Iandola, Raghuraman Krishnamoorthi, Bilge Soran, Vikas Chandra},
journal={preprint arXiv:2411.18933},
year={2024}
}
About
Efficient Track Anything
Resources
Readme
Apache-2.0 license
Activity
Stars
823 stars
Watchers
15 watching
Forks
44 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Efficient Track Anything
[📕Project][🤗Gradio Demo][📕Paper][🤗Checkpoints]
The Efficient Track Anything Model(EfficientTAM) takes a vanilla lightweight ViT image encoder. An efficient memory cross-attention is proposed to further improve the efficiency. Our EfficientTAMs are trained on SA-1B (image) and SA-V (video) datasets. EfficientTAM achieves comparable performance with SAM 2 with improved efficiency. Our EfficientTAM can run >10 frames per second with reasonable video segmentation performance on iPhone 15. Try our demo with a family of EfficientTAMs at [🤗Gradio Demo].
Efficient Track Anything design
News
[Jan.5 2025] We add the support for running Efficient Track Anything on Macs with MPS backend. Check the example app.py.
[Jan.3 2025] We update the codebase of Efficient Track Anything, adpated from the latest SAM2 codebase with improved inference efficiency. Check the latest SAM2 update on Dec. 11 2024 for details. Thanks to SAM 2 team!
Efficient Track Anything Speed Update
[Dec.22 2024] We release 🤗Efficient Track Anything Checkpoints.
[Dec.4 2024] 🤗Efficient Track Anything for segment everything. Thanks to @SkalskiP!
[Dec.2 2024] We provide the preliminary version of Efficient Track Anything for demonstration.
Online Demo & Examples
Online demo and examples can be found in the project page.
EfficientTAM Video Segmentation Examples
SAM 2SAM2
EfficientTAMEfficientTAM
EfficientTAM Image Segmentation Examples
Input Image, SAM, EficientSAM, SAM 2, EfficientTAM
Point-promptpoint-prompt
Box-promptbox-prompt
Segment everythingsegment everything
Model
EfficientTAM checkpoints are available at the Hugging Face Space.
Getting Started
Installation
git clone https://github.com/yformer/EfficientTAM.git
cd EfficientTAM
conda create -n efficient_track_anything python=3.12
conda activate efficient_track_anything
pip install -e .
Download Checkpoints
cd checkpoints
./download_checkpoints.sh
We can benchmark FPS of efficient track anything models on GPUs and model size.
FPS Benchmarking and Model Size
cd ..
python efficient_track_anything/benchmark.py
Launching Gradio Demo Locally
For efficient track anything video, run
python app.py

For efficient track anything image, run
python app_image.py

Building Efficient Track Anything
You can build efficient track anything model with a config and initial the model with a checkpoint,
importtorchfromefficient_track_anything.build_efficienttamimport (
build_efficienttam_video_predictor,
)
checkpoint="./checkpoints/efficienttam_s.pt"model_cfg="configs/efficienttam/efficienttam_s.yaml"predictor=build_efficienttam_video_predictor(model_cfg, checkpoint)
Efficient Track Anything Notebook Example
The notebook is shared here
License
Efficient track anything checkpoints and codebase are licensed under Apache 2.0.
Acknowledgement
If you're using Efficient Track Anything in your research or applications, please cite using this BibTeX:
@article{xiong2024efficienttam,
title={Efficient Track Anything},
author={Yunyang Xiong, Chong Zhou, Xiaoyu Xiang, Lemeng Wu, Chenchen Zhu, Zechun Liu, Saksham Suri, Balakrishnan Varadarajan, Ramya Akula, Forrest Iandola, Raghuraman Krishnamoorthi, Bilge Soran, Vikas Chandra},
journal={preprint arXiv:2411.18933},
year={2024}
}
About
Efficient Track Anything
Resources
Readme
Apache-2.0 license
Activity
Stars
823 stars
Watchers
15 watching
Forks
44 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Efficient Track Anything
[📕Project][🤗Gradio Demo][📕Paper][🤗Checkpoints]
The Efficient Track Anything Model(EfficientTAM) takes a vanilla lightweight ViT image encoder. An efficient memory cross-attention is proposed to further improve the efficiency. Our EfficientTAMs are trained on SA-1B (image) and SA-V (video) datasets. EfficientTAM achieves comparable performance with SAM 2 with improved efficiency. Our EfficientTAM can run >10 frames per second with reasonable video segmentation performance on iPhone 15. Try our demo with a family of EfficientTAMs at [🤗Gradio Demo].
Efficient Track Anything design
News
[Jan.5 2025] We add the support for running Efficient Track Anything on Macs with MPS backend. Check the example app.py.
[Jan.3 2025] We update the codebase of Efficient Track Anything, adpated from the latest SAM2 codebase with improved inference efficiency. Check the latest SAM2 update on Dec. 11 2024 for details. Thanks to SAM 2 team!
Efficient Track Anything Speed Update
[Dec.22 2024] We release 🤗Efficient Track Anything Checkpoints.
[Dec.4 2024] 🤗Efficient Track Anything for segment everything. Thanks to @SkalskiP!
[Dec.2 2024] We provide the preliminary version of Efficient Track Anything for demonstration.
Online Demo & Examples
Online demo and examples can be found in the project page.
EfficientTAM Video Segmentation Examples
SAM 2SAM2
EfficientTAMEfficientTAM
EfficientTAM Image Segmentation Examples
Input Image, SAM, EficientSAM, SAM 2, EfficientTAM
Point-promptpoint-prompt
Box-promptbox-prompt
Segment everythingsegment everything
Model
EfficientTAM checkpoints are available at the Hugging Face Space.
Getting Started
Installation
git clone https://github.com/yformer/EfficientTAM.git
cd EfficientTAM
conda create -n efficient_track_anything python=3.12
conda activate efficient_track_anything
pip install -e .
Download Checkpoints
cd checkpoints
./download_checkpoints.sh
We can benchmark FPS of efficient track anything models on GPUs and model size.
FPS Benchmarking and Model Size
cd ..
python efficient_track_anything/benchmark.py
Launching Gradio Demo Locally
For efficient track anything video, run
python app.py

For efficient track anything image, run
python app_image.py

Building Efficient Track Anything
You can build efficient track anything model with a config and initial the model with a checkpoint,
importtorchfromefficient_track_anything.build_efficienttamimport (
build_efficienttam_video_predictor,
)
checkpoint="./checkpoints/efficienttam_s.pt"model_cfg="configs/efficienttam/efficienttam_s.yaml"predictor=build_efficienttam_video_predictor(model_cfg, checkpoint)
Efficient Track Anything Notebook Example
The notebook is shared here
License
Efficient track anything checkpoints and codebase are licensed under Apache 2.0.
Acknowledgement
If you're using Efficient Track Anything in your research or applications, please cite using this BibTeX:
@article{xiong2024efficienttam,
title={Efficient Track Anything},
author={Yunyang Xiong, Chong Zhou, Xiaoyu Xiang, Lemeng Wu, Chenchen Zhu, Zechun Liu, Saksham Suri, Balakrishnan Varadarajan, Ramya Akula, Forrest Iandola, Raghuraman Krishnamoorthi, Bilge Soran, Vikas Chandra},
journal={preprint arXiv:2411.18933},
year={2024}
}
About
Efficient Track Anything
Resources
Readme
Apache-2.0 license
Activity
Stars
823 stars
Watchers
15 watching
Forks
44 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Efficient Track Anything
[📕Project][🤗Gradio Demo][📕Paper][🤗Checkpoints]
The Efficient Track Anything Model(EfficientTAM) takes a vanilla lightweight ViT image encoder. An efficient memory cross-attention is proposed to further improve the efficiency. Our EfficientTAMs are trained on SA-1B (image) and SA-V (video) datasets. EfficientTAM achieves comparable performance with SAM 2 with improved efficiency. Our EfficientTAM can run >10 frames per second with reasonable video segmentation performance on iPhone 15. Try our demo with a family of EfficientTAMs at [🤗Gradio Demo].
Efficient Track Anything design
News
[Jan.5 2025] We add the support for running Efficient Track Anything on Macs with MPS backend. Check the example app.py.
[Jan.3 2025] We update the codebase of Efficient Track Anything, adpated from the latest SAM2 codebase with improved inference efficiency. Check the latest SAM2 update on Dec. 11 2024 for details. Thanks to SAM 2 team!
Efficient Track Anything Speed Update
[Dec.22 2024] We release 🤗Efficient Track Anything Checkpoints.
[Dec.4 2024] 🤗Efficient Track Anything for segment everything. Thanks to @SkalskiP!
[Dec.2 2024] We provide the preliminary version of Efficient Track Anything for demonstration.
Online Demo & Examples
Online demo and examples can be found in the project page.
EfficientTAM Video Segmentation Examples
SAM 2SAM2
EfficientTAMEfficientTAM
EfficientTAM Image Segmentation Examples
Input Image, SAM, EficientSAM, SAM 2, EfficientTAM
Point-promptpoint-prompt
Box-promptbox-prompt
Segment everythingsegment everything
Model
EfficientTAM checkpoints are available at the Hugging Face Space.
Getting Started
Installation
git clone https://github.com/yformer/EfficientTAM.git
cd EfficientTAM
conda create -n efficient_track_anything python=3.12
conda activate efficient_track_anything
pip install -e .
Download Checkpoints
cd checkpoints
./download_checkpoints.sh
We can benchmark FPS of efficient track anything models on GPUs and model size.
FPS Benchmarking and Model Size
cd ..
python efficient_track_anything/benchmark.py
Launching Gradio Demo Locally
For efficient track anything video, run
python app.py

For efficient track anything image, run
python app_image.py

Building Efficient Track Anything
You can build efficient track anything model with a config and initial the model with a checkpoint,
importtorchfromefficient_track_anything.build_efficienttamimport (
build_efficienttam_video_predictor,
)
checkpoint="./checkpoints/efficienttam_s.pt"model_cfg="configs/efficienttam/efficienttam_s.yaml"predictor=build_efficienttam_video_predictor(model_cfg, checkpoint)
Efficient Track Anything Notebook Example
The notebook is shared here
License
Efficient track anything checkpoints and codebase are licensed under Apache 2.0.
Acknowledgement
If you're using Efficient Track Anything in your research or applications, please cite using this BibTeX:
@article{xiong2024efficienttam,
title={Efficient Track Anything},
author={Yunyang Xiong, Chong Zhou, Xiaoyu Xiang, Lemeng Wu, Chenchen Zhu, Zechun Liu, Saksham Suri, Balakrishnan Varadarajan, Ramya Akula, Forrest Iandola, Raghuraman Krishnamoorthi, Bilge Soran, Vikas Chandra},
journal={preprint arXiv:2411.18933},
year={2024}
}
About
Efficient Track Anything
Resources
Readme
Apache-2.0 license
Activity
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823 stars
Watchers
15 watching
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44 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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Repository files navigation
Efficient Track Anything
[📕Project][🤗Gradio Demo][📕Paper][🤗Checkpoints]
The Efficient Track Anything Model(EfficientTAM) takes a vanilla lightweight ViT image encoder. An efficient memory cross-attention is proposed to further improve the efficiency. Our EfficientTAMs are trained on SA-1B (image) and SA-V (video) datasets. EfficientTAM achieves comparable performance with SAM 2 with improved efficiency. Our EfficientTAM can run >10 frames per second with reasonable video segmentation performance on iPhone 15. Try our demo with a family of EfficientTAMs at [🤗Gradio Demo].
Efficient Track Anything design
News
[Jan.5 2025] We add the support for running Efficient Track Anything on Macs with MPS backend. Check the example app.py.
[Jan.3 2025] We update the codebase of Efficient Track Anything, adpated from the latest SAM2 codebase with improved inference efficiency. Check the latest SAM2 update on Dec. 11 2024 for details. Thanks to SAM 2 team!
Efficient Track Anything Speed Update
[Dec.22 2024] We release 🤗Efficient Track Anything Checkpoints.
[Dec.4 2024] 🤗Efficient Track Anything for segment everything. Thanks to @SkalskiP!
[Dec.2 2024] We provide the preliminary version of Efficient Track Anything for demonstration.
Online Demo & Examples
Online demo and examples can be found in the project page.
EfficientTAM Video Segmentation Examples
SAM 2SAM2
EfficientTAMEfficientTAM
EfficientTAM Image Segmentation Examples
Input Image, SAM, EficientSAM, SAM 2, EfficientTAM
Point-promptpoint-prompt
Box-promptbox-prompt
Segment everythingsegment everything
Model
EfficientTAM checkpoints are available at the Hugging Face Space.
Getting Started
Installation
git clone https://github.com/yformer/EfficientTAM.git
cd EfficientTAM
conda create -n efficient_track_anything python=3.12
conda activate efficient_track_anything
pip install -e .
Download Checkpoints
cd checkpoints
./download_checkpoints.sh
We can benchmark FPS of efficient track anything models on GPUs and model size.
FPS Benchmarking and Model Size
cd ..
python efficient_track_anything/benchmark.py
Launching Gradio Demo Locally
For efficient track anything video, run
python app.py

For efficient track anything image, run
python app_image.py

Building Efficient Track Anything
You can build efficient track anything model with a config and initial the model with a checkpoint,
importtorchfromefficient_track_anything.build_efficienttamimport (
build_efficienttam_video_predictor,
)
checkpoint="./checkpoints/efficienttam_s.pt"model_cfg="configs/efficienttam/efficienttam_s.yaml"predictor=build_efficienttam_video_predictor(model_cfg, checkpoint)
Efficient Track Anything Notebook Example
The notebook is shared here
License
Efficient track anything checkpoints and codebase are licensed under Apache 2.0.
Acknowledgement
If you're using Efficient Track Anything in your research or applications, please cite using this BibTeX:
@article{xiong2024efficienttam,
title={Efficient Track Anything},
author={Yunyang Xiong, Chong Zhou, Xiaoyu Xiang, Lemeng Wu, Chenchen Zhu, Zechun Liu, Saksham Suri, Balakrishnan Varadarajan, Ramya Akula, Forrest Iandola, Raghuraman Krishnamoorthi, Bilge Soran, Vikas Chandra},
journal={preprint arXiv:2411.18933},
year={2024}
}
About
Efficient Track Anything
Resources
Readme
Apache-2.0 license
Activity
Stars
823 stars
Watchers
15 watching
Forks
44 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
Efficient Track Anything
[📕Project][🤗Gradio Demo][📕Paper][🤗Checkpoints]
The Efficient Track Anything Model(EfficientTAM) takes a vanilla lightweight ViT image encoder. An efficient memory cross-attention is proposed to further improve the efficiency. Our EfficientTAMs are trained on SA-1B (image) and SA-V (video) datasets. EfficientTAM achieves comparable performance with SAM 2 with improved efficiency. Our EfficientTAM can run >10 frames per second with reasonable video segmentation performance on iPhone 15. Try our demo with a family of EfficientTAMs at [🤗Gradio Demo].
Efficient Track Anything design
News
[Jan.5 2025] We add the support for running Efficient Track Anything on Macs with MPS backend. Check the example app.py.
[Jan.3 2025] We update the codebase of Efficient Track Anything, adpated from the latest SAM2 codebase with improved inference efficiency. Check the latest SAM2 update on Dec. 11 2024 for details. Thanks to SAM 2 team!
Efficient Track Anything Speed Update
[Dec.22 2024] We release 🤗Efficient Track Anything Checkpoints.
[Dec.4 2024] 🤗Efficient Track Anything for segment everything. Thanks to @SkalskiP!
[Dec.2 2024] We provide the preliminary version of Efficient Track Anything for demonstration.
Online Demo & Examples
Online demo and examples can be found in the project page.
EfficientTAM Video Segmentation Examples
SAM 2SAM2
EfficientTAMEfficientTAM
EfficientTAM Image Segmentation Examples
Input Image, SAM, EficientSAM, SAM 2, EfficientTAM
Point-promptpoint-prompt
Box-promptbox-prompt
Segment everythingsegment everything
Model
EfficientTAM checkpoints are available at the Hugging Face Space.
Getting Started
Installation
git clone https://github.com/yformer/EfficientTAM.git
cd EfficientTAM
conda create -n efficient_track_anything python=3.12
conda activate efficient_track_anything
pip install -e .
Download Checkpoints
cd checkpoints
./download_checkpoints.sh
We can benchmark FPS of efficient track anything models on GPUs and model size.
FPS Benchmarking and Model Size
cd ..
python efficient_track_anything/benchmark.py
Launching Gradio Demo Locally
For efficient track anything video, run
python app.py

For efficient track anything image, run
python app_image.py

Building Efficient Track Anything
You can build efficient track anything model with a config and initial the model with a checkpoint,
importtorchfromefficient_track_anything.build_efficienttamimport (
build_efficienttam_video_predictor,
)
checkpoint="./checkpoints/efficienttam_s.pt"model_cfg="configs/efficienttam/efficienttam_s.yaml"predictor=build_efficienttam_video_predictor(model_cfg, checkpoint)
Efficient Track Anything Notebook Example
The notebook is shared here
License
Efficient track anything checkpoints and codebase are licensed under Apache 2.0.
Acknowledgement
If you're using Efficient Track Anything in your research or applications, please cite using this BibTeX:
@article{xiong2024efficienttam,
title={Efficient Track Anything},
author={Yunyang Xiong, Chong Zhou, Xiaoyu Xiang, Lemeng Wu, Chenchen Zhu, Zechun Liu, Saksham Suri, Balakrishnan Varadarajan, Ramya Akula, Forrest Iandola, Raghuraman Krishnamoorthi, Bilge Soran, Vikas Chandra},
journal={preprint arXiv:2411.18933},
year={2024}
}
About
Efficient Track Anything
Resources
Readme
Apache-2.0 license
Activity
Stars
823 stars
Watchers
15 watching
Forks
44 forks
Report repository
Releases
Packages
Used by
Contributors
Languages