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SeqMatchNet

Code for the CoRL 2021 Oral paper "SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition and Relocalization"

[OpenReview] [PDF] [CoRL 2021 YouTube Video]


SeqMatchNet: Contrastive Learning with Sequence Matching.

Setup

Conda

conda create -n seqnet numpy pytorch=1.8.0 torchvision tqdm scikit-learn faiss tensorboardx h5py wandb -c pytorch -c conda-forge

Download

Run bash download.sh to download single image NetVLAD descriptors (3.4 GB) for the Nordland-clean dataset [a] and the Oxford dataset (0.3 GB) [b].

You can download model trained on Oxford from here.

Run

Train

To train SeqMatchNet on the Oxford dataset with both the loss and negative mining based on sequence matching:

pythonmain.py--modetrain--seqL5--pooling--datasetoxford-v1.0--loss_trip_methodmeanOfPairs--neg_trip_methodmeanOfPairs--expNameox10_MoP_negMoP

For the Nordland dataset:

pythonmain.py--modetrain--seqL5--pooling--datasetnordland-sw--loss_trip_methodmeanOfPairs--neg_trip_methodmeanOfPairs--expNamenord-sw_MoP_negMoP

To train without sequence matching:

pythonmain.py--modetrain--seqL5--pooling--datasetoxford-v1.0--loss_trip_methodcenterOnly--neg_trip_methodcenterOnly--expNameox10_CO_negCO

Test

pythonmain.py--modetest--seqL5--pooling-datasetoxford-v1.0--splittest--resume ./data/runs/<name_of_the_model_file>

Acknowledgement

The code in this repository is based on oravus/seqNet and Nanne/pytorch-NetVlad.

Citation

@inproceedings{garg2021seqmatchnet,
title={SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition \& Relocalization},
author={Garg, Sourav and Vankadari, Madhu and Milford, Michael},
booktitle={5th Annual Conference on Robot Learning},
year={2021}
}

Other Related Projects

SeqNet; Delta Descriptors (2020); Patch-NetVLAD (2021); CoarseHash (2020); seq2single (2019); LoST (2018)

[a] This is the clean version of the dataset that excludes images from the tunnels and red lights and can be downloaded from here.

[b] These will automatically save to ./data/, you can modify this path in download.sh and get_datasets.py to specify your workdir.

About

Code for the CoRL 2021 paper "SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition \& Relocalization"

Topics

contrastive-learningday-nightlocalizationplace-recognitionrelocalizationsequence-matchingvisual-place-recognition

Resources

Readme
MIT license
Activity

Stars

59 stars

Watchers

2 watching

Forks

3 forks
Report repository

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SeqMatchNet
Code for the CoRL 2021 Oral paper "SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition and Relocalization"
[OpenReview] [PDF] [CoRL 2021 YouTube Video]

SeqMatchNet: Contrastive Learning with Sequence Matching.
Setup
Conda
conda create -n seqnet numpy pytorch=1.8.0 torchvision tqdm scikit-learn faiss tensorboardx h5py wandb -c pytorch -c conda-forge
Download
Run bash download.sh to download single image NetVLAD descriptors (3.4 GB) for the Nordland-clean dataset [a] and the Oxford dataset (0.3 GB) [b].
You can download model trained on Oxford from here.
Run
Train
To train SeqMatchNet on the Oxford dataset with both the loss and negative mining based on sequence matching:
pythonmain.py--modetrain--seqL5--pooling--datasetoxford-v1.0--loss_trip_methodmeanOfPairs--neg_trip_methodmeanOfPairs--expNameox10_MoP_negMoP
For the Nordland dataset:
pythonmain.py--modetrain--seqL5--pooling--datasetnordland-sw--loss_trip_methodmeanOfPairs--neg_trip_methodmeanOfPairs--expNamenord-sw_MoP_negMoP
To train without sequence matching:
pythonmain.py--modetrain--seqL5--pooling--datasetoxford-v1.0--loss_trip_methodcenterOnly--neg_trip_methodcenterOnly--expNameox10_CO_negCO
Test
pythonmain.py--modetest--seqL5--pooling-datasetoxford-v1.0--splittest--resume ./data/runs/<name_of_the_model_file>
Acknowledgement
The code in this repository is based on oravus/seqNet and Nanne/pytorch-NetVlad.
Citation
@inproceedings{garg2021seqmatchnet,
title={SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition \& Relocalization},
author={Garg, Sourav and Vankadari, Madhu and Milford, Michael},
booktitle={5th Annual Conference on Robot Learning},
year={2021}
}

Other Related Projects
SeqNet;
Delta Descriptors (2020);
Patch-NetVLAD (2021);
CoarseHash (2020);
seq2single (2019);
LoST (2018)
[a] This is the clean version of the dataset that excludes images from the tunnels and red lights and can be downloaded from here.
[b] These will automatically save to ./data/, you can modify this path in download.sh and get_datasets.py to specify your workdir.
About
Code for the CoRL 2021 paper "SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition \& Relocalization"
Topics
contrastive-learningday-nightlocalizationplace-recognitionrelocalizationsequence-matchingvisual-place-recognition
Resources
Readme
MIT license
Activity
Stars
59 stars
Watchers
2 watching
Forks
3 forks
Report repository
Releases
Packages
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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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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SeqMatchNet
Code for the CoRL 2021 Oral paper "SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition and Relocalization"
[OpenReview] [PDF] [CoRL 2021 YouTube Video]

SeqMatchNet: Contrastive Learning with Sequence Matching.
Setup
Conda
conda create -n seqnet numpy pytorch=1.8.0 torchvision tqdm scikit-learn faiss tensorboardx h5py wandb -c pytorch -c conda-forge
Download
Run bash download.sh to download single image NetVLAD descriptors (3.4 GB) for the Nordland-clean dataset [a] and the Oxford dataset (0.3 GB) [b].
You can download model trained on Oxford from here.
Run
Train
To train SeqMatchNet on the Oxford dataset with both the loss and negative mining based on sequence matching:
pythonmain.py--modetrain--seqL5--pooling--datasetoxford-v1.0--loss_trip_methodmeanOfPairs--neg_trip_methodmeanOfPairs--expNameox10_MoP_negMoP
For the Nordland dataset:
pythonmain.py--modetrain--seqL5--pooling--datasetnordland-sw--loss_trip_methodmeanOfPairs--neg_trip_methodmeanOfPairs--expNamenord-sw_MoP_negMoP
To train without sequence matching:
pythonmain.py--modetrain--seqL5--pooling--datasetoxford-v1.0--loss_trip_methodcenterOnly--neg_trip_methodcenterOnly--expNameox10_CO_negCO
Test
pythonmain.py--modetest--seqL5--pooling-datasetoxford-v1.0--splittest--resume ./data/runs/<name_of_the_model_file>
Acknowledgement
The code in this repository is based on oravus/seqNet and Nanne/pytorch-NetVlad.
Citation
@inproceedings{garg2021seqmatchnet,
title={SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition \& Relocalization},
author={Garg, Sourav and Vankadari, Madhu and Milford, Michael},
booktitle={5th Annual Conference on Robot Learning},
year={2021}
}

Other Related Projects
SeqNet;
Delta Descriptors (2020);
Patch-NetVLAD (2021);
CoarseHash (2020);
seq2single (2019);
LoST (2018)
[a] This is the clean version of the dataset that excludes images from the tunnels and red lights and can be downloaded from here.
[b] These will automatically save to ./data/, you can modify this path in download.sh and get_datasets.py to specify your workdir.
About
Code for the CoRL 2021 paper "SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition \& Relocalization"
Topics
contrastive-learningday-nightlocalizationplace-recognitionrelocalizationsequence-matchingvisual-place-recognition
Resources
Readme
MIT license
Activity
Stars
59 stars
Watchers
2 watching
Forks
3 forks
Report repository
Releases
Packages
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");
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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SeqMatchNet
Code for the CoRL 2021 Oral paper "SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition and Relocalization"
[OpenReview] [PDF] [CoRL 2021 YouTube Video]

SeqMatchNet: Contrastive Learning with Sequence Matching.
Setup
Conda
conda create -n seqnet numpy pytorch=1.8.0 torchvision tqdm scikit-learn faiss tensorboardx h5py wandb -c pytorch -c conda-forge
Download
Run bash download.sh to download single image NetVLAD descriptors (3.4 GB) for the Nordland-clean dataset [a] and the Oxford dataset (0.3 GB) [b].
You can download model trained on Oxford from here.
Run
Train
To train SeqMatchNet on the Oxford dataset with both the loss and negative mining based on sequence matching:
pythonmain.py--modetrain--seqL5--pooling--datasetoxford-v1.0--loss_trip_methodmeanOfPairs--neg_trip_methodmeanOfPairs--expNameox10_MoP_negMoP
For the Nordland dataset:
pythonmain.py--modetrain--seqL5--pooling--datasetnordland-sw--loss_trip_methodmeanOfPairs--neg_trip_methodmeanOfPairs--expNamenord-sw_MoP_negMoP
To train without sequence matching:
pythonmain.py--modetrain--seqL5--pooling--datasetoxford-v1.0--loss_trip_methodcenterOnly--neg_trip_methodcenterOnly--expNameox10_CO_negCO
Test
pythonmain.py--modetest--seqL5--pooling-datasetoxford-v1.0--splittest--resume ./data/runs/<name_of_the_model_file>
Acknowledgement
The code in this repository is based on oravus/seqNet and Nanne/pytorch-NetVlad.
Citation
@inproceedings{garg2021seqmatchnet,
title={SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition \& Relocalization},
author={Garg, Sourav and Vankadari, Madhu and Milford, Michael},
booktitle={5th Annual Conference on Robot Learning},
year={2021}
}

Other Related Projects
SeqNet;
Delta Descriptors (2020);
Patch-NetVLAD (2021);
CoarseHash (2020);
seq2single (2019);
LoST (2018)
[a] This is the clean version of the dataset that excludes images from the tunnels and red lights and can be downloaded from here.
[b] These will automatically save to ./data/, you can modify this path in download.sh and get_datasets.py to specify your workdir.
About
Code for the CoRL 2021 paper "SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition \& Relocalization"
Topics
contrastive-learningday-nightlocalizationplace-recognitionrelocalizationsequence-matchingvisual-place-recognition
Resources
Readme
MIT license
Activity
Stars
59 stars
Watchers
2 watching
Forks
3 forks
Report repository
Releases
Packages
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");
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} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
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var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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SeqMatchNet
Code for the CoRL 2021 Oral paper "SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition and Relocalization"
[OpenReview] [PDF] [CoRL 2021 YouTube Video]

SeqMatchNet: Contrastive Learning with Sequence Matching.
Setup
Conda
conda create -n seqnet numpy pytorch=1.8.0 torchvision tqdm scikit-learn faiss tensorboardx h5py wandb -c pytorch -c conda-forge
Download
Run bash download.sh to download single image NetVLAD descriptors (3.4 GB) for the Nordland-clean dataset [a] and the Oxford dataset (0.3 GB) [b].
You can download model trained on Oxford from here.
Run
Train
To train SeqMatchNet on the Oxford dataset with both the loss and negative mining based on sequence matching:
pythonmain.py--modetrain--seqL5--pooling--datasetoxford-v1.0--loss_trip_methodmeanOfPairs--neg_trip_methodmeanOfPairs--expNameox10_MoP_negMoP
For the Nordland dataset:
pythonmain.py--modetrain--seqL5--pooling--datasetnordland-sw--loss_trip_methodmeanOfPairs--neg_trip_methodmeanOfPairs--expNamenord-sw_MoP_negMoP
To train without sequence matching:
pythonmain.py--modetrain--seqL5--pooling--datasetoxford-v1.0--loss_trip_methodcenterOnly--neg_trip_methodcenterOnly--expNameox10_CO_negCO
Test
pythonmain.py--modetest--seqL5--pooling-datasetoxford-v1.0--splittest--resume ./data/runs/<name_of_the_model_file>
Acknowledgement
The code in this repository is based on oravus/seqNet and Nanne/pytorch-NetVlad.
Citation
@inproceedings{garg2021seqmatchnet,
title={SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition \& Relocalization},
author={Garg, Sourav and Vankadari, Madhu and Milford, Michael},
booktitle={5th Annual Conference on Robot Learning},
year={2021}
}

Other Related Projects
SeqNet;
Delta Descriptors (2020);
Patch-NetVLAD (2021);
CoarseHash (2020);
seq2single (2019);
LoST (2018)
[a] This is the clean version of the dataset that excludes images from the tunnels and red lights and can be downloaded from here.
[b] These will automatically save to ./data/, you can modify this path in download.sh and get_datasets.py to specify your workdir.
About
Code for the CoRL 2021 paper "SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition \& Relocalization"
Topics
contrastive-learningday-nightlocalizationplace-recognitionrelocalizationsequence-matchingvisual-place-recognition
Resources
Readme
MIT license
Activity
Stars
59 stars
Watchers
2 watching
Forks
3 forks
Report repository
Releases
Packages
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");
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} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
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SeqMatchNet
Code for the CoRL 2021 Oral paper "SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition and Relocalization"
[OpenReview] [PDF] [CoRL 2021 YouTube Video]

SeqMatchNet: Contrastive Learning with Sequence Matching.
Setup
Conda
conda create -n seqnet numpy pytorch=1.8.0 torchvision tqdm scikit-learn faiss tensorboardx h5py wandb -c pytorch -c conda-forge
Download
Run bash download.sh to download single image NetVLAD descriptors (3.4 GB) for the Nordland-clean dataset [a] and the Oxford dataset (0.3 GB) [b].
You can download model trained on Oxford from here.
Run
Train
To train SeqMatchNet on the Oxford dataset with both the loss and negative mining based on sequence matching:
pythonmain.py--modetrain--seqL5--pooling--datasetoxford-v1.0--loss_trip_methodmeanOfPairs--neg_trip_methodmeanOfPairs--expNameox10_MoP_negMoP
For the Nordland dataset:
pythonmain.py--modetrain--seqL5--pooling--datasetnordland-sw--loss_trip_methodmeanOfPairs--neg_trip_methodmeanOfPairs--expNamenord-sw_MoP_negMoP
To train without sequence matching:
pythonmain.py--modetrain--seqL5--pooling--datasetoxford-v1.0--loss_trip_methodcenterOnly--neg_trip_methodcenterOnly--expNameox10_CO_negCO
Test
pythonmain.py--modetest--seqL5--pooling-datasetoxford-v1.0--splittest--resume ./data/runs/<name_of_the_model_file>
Acknowledgement
The code in this repository is based on oravus/seqNet and Nanne/pytorch-NetVlad.
Citation
@inproceedings{garg2021seqmatchnet,
title={SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition \& Relocalization},
author={Garg, Sourav and Vankadari, Madhu and Milford, Michael},
booktitle={5th Annual Conference on Robot Learning},
year={2021}
}

Other Related Projects
SeqNet;
Delta Descriptors (2020);
Patch-NetVLAD (2021);
CoarseHash (2020);
seq2single (2019);
LoST (2018)
[a] This is the clean version of the dataset that excludes images from the tunnels and red lights and can be downloaded from here.
[b] These will automatically save to ./data/, you can modify this path in download.sh and get_datasets.py to specify your workdir.
About
Code for the CoRL 2021 paper "SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition \& Relocalization"
Topics
contrastive-learningday-nightlocalizationplace-recognitionrelocalizationsequence-matchingvisual-place-recognition
Resources
Readme
MIT license
Activity
Stars
59 stars
Watchers
2 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors
Languages

, '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");
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} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
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SeqMatchNet
Code for the CoRL 2021 Oral paper "SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition and Relocalization"
[OpenReview] [PDF] [CoRL 2021 YouTube Video]

SeqMatchNet: Contrastive Learning with Sequence Matching.
Setup
Conda
conda create -n seqnet numpy pytorch=1.8.0 torchvision tqdm scikit-learn faiss tensorboardx h5py wandb -c pytorch -c conda-forge
Download
Run bash download.sh to download single image NetVLAD descriptors (3.4 GB) for the Nordland-clean dataset [a] and the Oxford dataset (0.3 GB) [b].
You can download model trained on Oxford from here.
Run
Train
To train SeqMatchNet on the Oxford dataset with both the loss and negative mining based on sequence matching:
pythonmain.py--modetrain--seqL5--pooling--datasetoxford-v1.0--loss_trip_methodmeanOfPairs--neg_trip_methodmeanOfPairs--expNameox10_MoP_negMoP
For the Nordland dataset:
pythonmain.py--modetrain--seqL5--pooling--datasetnordland-sw--loss_trip_methodmeanOfPairs--neg_trip_methodmeanOfPairs--expNamenord-sw_MoP_negMoP
To train without sequence matching:
pythonmain.py--modetrain--seqL5--pooling--datasetoxford-v1.0--loss_trip_methodcenterOnly--neg_trip_methodcenterOnly--expNameox10_CO_negCO
Test
pythonmain.py--modetest--seqL5--pooling-datasetoxford-v1.0--splittest--resume ./data/runs/<name_of_the_model_file>
Acknowledgement
The code in this repository is based on oravus/seqNet and Nanne/pytorch-NetVlad.
Citation
@inproceedings{garg2021seqmatchnet,
title={SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition \& Relocalization},
author={Garg, Sourav and Vankadari, Madhu and Milford, Michael},
booktitle={5th Annual Conference on Robot Learning},
year={2021}
}

Other Related Projects
SeqNet;
Delta Descriptors (2020);
Patch-NetVLAD (2021);
CoarseHash (2020);
seq2single (2019);
LoST (2018)
[a] This is the clean version of the dataset that excludes images from the tunnels and red lights and can be downloaded from here.
[b] These will automatically save to ./data/, you can modify this path in download.sh and get_datasets.py to specify your workdir.
About
Code for the CoRL 2021 paper "SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition \& Relocalization"
Topics
contrastive-learningday-nightlocalizationplace-recognitionrelocalizationsequence-matchingvisual-place-recognition
Resources
Readme
MIT license
Activity
Stars
59 stars
Watchers
2 watching
Forks
3 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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SeqMatchNet
Code for the CoRL 2021 Oral paper "SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition and Relocalization"
[OpenReview] [PDF] [CoRL 2021 YouTube Video]

SeqMatchNet: Contrastive Learning with Sequence Matching.
Setup
Conda
conda create -n seqnet numpy pytorch=1.8.0 torchvision tqdm scikit-learn faiss tensorboardx h5py wandb -c pytorch -c conda-forge
Download
Run bash download.sh to download single image NetVLAD descriptors (3.4 GB) for the Nordland-clean dataset [a] and the Oxford dataset (0.3 GB) [b].
You can download model trained on Oxford from here.
Run
Train
To train SeqMatchNet on the Oxford dataset with both the loss and negative mining based on sequence matching:
pythonmain.py--modetrain--seqL5--pooling--datasetoxford-v1.0--loss_trip_methodmeanOfPairs--neg_trip_methodmeanOfPairs--expNameox10_MoP_negMoP
For the Nordland dataset:
pythonmain.py--modetrain--seqL5--pooling--datasetnordland-sw--loss_trip_methodmeanOfPairs--neg_trip_methodmeanOfPairs--expNamenord-sw_MoP_negMoP
To train without sequence matching:
pythonmain.py--modetrain--seqL5--pooling--datasetoxford-v1.0--loss_trip_methodcenterOnly--neg_trip_methodcenterOnly--expNameox10_CO_negCO
Test
pythonmain.py--modetest--seqL5--pooling-datasetoxford-v1.0--splittest--resume ./data/runs/<name_of_the_model_file>
Acknowledgement
The code in this repository is based on oravus/seqNet and Nanne/pytorch-NetVlad.
Citation
@inproceedings{garg2021seqmatchnet,
title={SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition \& Relocalization},
author={Garg, Sourav and Vankadari, Madhu and Milford, Michael},
booktitle={5th Annual Conference on Robot Learning},
year={2021}
}

Other Related Projects
SeqNet;
Delta Descriptors (2020);
Patch-NetVLAD (2021);
CoarseHash (2020);
seq2single (2019);
LoST (2018)
[a] This is the clean version of the dataset that excludes images from the tunnels and red lights and can be downloaded from here.
[b] These will automatically save to ./data/, you can modify this path in download.sh and get_datasets.py to specify your workdir.
About
Code for the CoRL 2021 paper "SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition \& Relocalization"
Topics
contrastive-learningday-nightlocalizationplace-recognitionrelocalizationsequence-matchingvisual-place-recognition
Resources
Readme
MIT license
Activity
Stars
59 stars
Watchers
2 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors
Languages