Synopsis of Social media discussions
Throughout the discussions, contributors use words like 'excited,' 'support,' and 'relevant,' emphasizing enthusiasm and recognizing the significance of the work. They mention specific details like 'segmentation nets,' 'recognition of materials,' and the 'Vector-LabPics Dataset,' which highlight a keen interest in both the technical methods and potential real-world applications, driving the overall positive and engaged tone.
Agreement
Moderate agreementMost posts express enthusiasm and support for the research, highlighting its potential applications, indicating broad agreement on the importance of this work.
Interest
High level of interestDiscussions demonstrate high curiosity, with commenters intrigued by the technical aspects and potential benefits for automating lab processes.
Engagement
High engagementPosts reference specific methods, datasets, and applications, showing deep engagement with the details of the research.
Impact
Moderate level of impactWhile generally positive, some focus on potential implications rather than immediate transformative effects, suggesting moderate but meaningful impact.
Social Mentions
YouTube
19 Videos
33 Posts
Blogs
2 Articles
Metrics
Video Views
8,321
Total Likes
209
Extended Reach
248,376
Social Features
54
Timeline: Posts about article
Top Social Media Posts
Alan Aspuru-Guzik
Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevant for automating chemistry
view full post
The Journal Co-operative
New work shows AI can identify materials in flasks, beakers, etc.!
TL;DR: they use segmentation nets to classify different regions of an image based on vessel and material type!
https://t.co/McU3czWaeG
#MachineLearning #ChemTwitter #RealTimeChem @A_Aspuru_Guzik https://t.co/IPP6EcifZu
view full post
ChemRxiv
Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset by Alan Aspuru-Guzik & co-workers
https://t.co/78ha8lZB0p https://t.co/8qVvMbDhOT
view full post
ACS Central Science
Check out our new issue here: https://t.co/mwB5Zon1MD
Supplementary cover story by Sagi Eppel, @A_Aspuru_Guzik & co-workers @chemuoft: https://t.co/TLVonyzjHd https://t.co/NRuPDzHMbJ
view full post
ACS Central Science
#ASAP by Sagi Eppel, @A_Aspuru_Guzik & co-workers @chemuoft!
A computer vision system for recognition materials and vessels in the chemistry lab. The system is based on the new LabPics image data set and convolutional neural nets for image segmentation: https://t.co/ZMjZJBzmus https://t.co/PpegqPxgMW
view full post
Posts referencing the article
Computer Vision for Liquid Level Detection in Vessels Using CNNs
This video introduces a machine learning approach for recognizing materials and vessels in chemistry labs using computer vision, supported by a new dataset called VectorLabPics, which contains 2187 annotated images of transparent containers and their contents.
June 10, 2018
2,480 views
Computer Vision for Material and Vessel Recognition in Chemistry Labs
This work introduces a machine learning approach for recognizing materials and vessels in chemistry labs using computer vision, supported by a new dataset called VectorLabPics, which contains 2187 annotated images of transparent containers and their contents. The dataset includes detailed annotations for vessels, materials
March 3, 2020
801 views
Liquid and Vessel Detection in Chemistry Labs Using CNNs
This video introduces a machine learning approach for recognizing materials and vessels in chemistry labs using computer vision, supported by the VectorLabPics dataset, which contains annotated images of vessels and their contents for neural network training.
September 28, 2018
758 views
Automatic Detection and Segmentation of Phase Separation in Liquids Using Computer Vision
This video demonstrates a machine learning approach to recognize and segment phase-separating liquids in transparent glasses, utilizing convolutional neural networks for instance segmentation. The models accurately detect vessels and material phases, supported by a comprehensive dataset.
March 3, 2020
492 views
Computer Vision for Liquid Level and Vessel Detection in Medical Infusions
Detecting liquid region level and vessel for infusion bottles iv bags blood bags and infusion bags using computer vision with convolutional neural nets for semantic segmentation.
March 3, 2020
453 views
Detecting Liquid and Solid Phases During Phase Transitions Using Computer Vision
Detecting liquid and solid phases in phase transition melting freezing utilizing computer vision with convolutional neural networks for semantic segmentation. The detected region is marked in purple, with material class names displayed in green. This approach supports chemistry laboratory analysis and is part of the compute
March 3, 2020
327 views
Automated Detection of Liquid and Vessel Regions in Medical Urine Samples Using Computer Vision
This video demonstrates the use of convolutional neural networks for semantic segmentation to detect liquid level and vessel regions in urine samples. The detected regions are marked in purple, supporting automated analysis in medical laboratories. Code and models are available online.
March 3, 2020
317 views
Detecting Precipitation in Solutions Using CNN-Based Computer Vision
Detecting precipitation and suspension in solutions with convolutional neural networks for semantic segmentation. The detected regions are marked in purple, and material class names are shown in green. The technique utilizes a fully convolutional neural network, with code and models available online.
March 3, 2020
281 views
Neural Network-Based Detection of Materials in Chemistry Containers
This video demonstrates computer vision techniques using convolutional neural networks to detect and segment liquids, solids, powders, and suspensions in transparent containers. The methods leverage a new dataset for recognizing materials and vessel components in chemistry labs.
February 18, 2020
275 views
Computer Vision for Segmenting Phase-Separating Liquids and Vessels in Chemistry
March 3, 2020
251 views
Detecting/segmentation foam and liquid in image using computer vision with convolutional nets
March 3, 2020
248 views
Computer vision for detecting materials in glassware vessels in the chemistry lab.
March 3, 2020
248 views
Detecting phase separation of liquids in a glass tube using computer vision with convolutional net
March 3, 2020
216 views
Detecting of liquid pouring and precipitation/suspension in a solution using computer vision
March 3, 2020
170 views
Detecting/segmentation of precipitation/suspension and lwith computer vision
March 3, 2020
167 views
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RT @TheJournalCoop: New work shows AI can identify materials in flasks, beakers, etc.! TL;DR: they use segmentation nets to classify diffe…
view full postNovember 27, 2020
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Automated Synthesis Forum
@UK_ASF (Twitter)RT @TheJournalCoop: New work shows AI can identify materials in flasks, beakers, etc.! TL;DR: they use segmentation nets to classify diffe…
view full postNovember 26, 2020
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Alan Aspuru-Guzik
@A_Aspuru_Guzik (Twitter)RT @TheJournalCoop: New work shows AI can identify materials in flasks, beakers, etc.! TL;DR: they use segmentation nets to classify diffe…
view full postNovember 26, 2020
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The Journal Co-operative
@TheJournalCoop (Twitter)New work shows AI can identify materials in flasks, beakers, etc.! TL;DR: they use segmentation nets to classify different regions of an image based on vessel and material type! https://t.co/McU3czWaeG #MachineLearning #ChemTwitter #RealTimeChem @A_Aspuru_Guzik https://t.co/IPP6EcifZu
view full postNovember 26, 2020
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ACS Central Science
@ACSCentSci (Twitter)Check out our new issue here: https://t.co/mwB5Zon1MD Supplementary cover story by Sagi Eppel, @A_Aspuru_Guzik & co-workers @chemuoft: https://t.co/TLVonyzjHd https://t.co/NRuPDzHMbJ
view full postNovember 10, 2020
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Alan Aspuru-Guzik
@A_Aspuru_Guzik (Twitter)RT @ACSCentSci: #ASAP by Sagi Eppel, @A_Aspuru_Guzik & co-workers @chemuoft! A computer vision system for recognition materials and vessel…
view full postSeptember 18, 2020
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ACS Central Science
@ACSCentSci (Twitter)#ASAP by Sagi Eppel, @A_Aspuru_Guzik & co-workers @chemuoft! A computer vision system for recognition materials and vessels in the chemistry lab. The system is based on the new LabPics image data set and convolutional neural nets for image segmentation: https://t.co/ZMjZJBzmus https://t.co/PpegqPxgMW
view full postSeptember 18, 2020
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Dial-a-Molecule
@DialaMolecule (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 15, 2020
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Automated Synthesis Forum
@UK_ASF (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 13, 2020
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'Aztec Eagle' Turbo
@trblnyx (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 13, 2020
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Diego Onna
@DiegoOnna (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 12, 2020
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Willi
@willigo09 (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 12, 2020
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Miguel Robles
@mikibrd (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 12, 2020
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Camilo Ruiz USAL
@copilco1 (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 12, 2020
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David Balcells
@BalcellsD (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 12, 2020
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Vittorio Saggiomo (@Vsaggiomo@bsky.social)
@V_Saggiomo (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 12, 2020
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Andrés De Bon Marche
@Jose_Andres_Mtz (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 11, 2020
24
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Haibin SU 蘇海斌
@Laviebay (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 11, 2020
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Edmundo Molina
@EdmundoMolinaMx (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 11, 2020
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Isaac Tamblyn
@itamblyn (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 11, 2020
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LaChicatanaVengativa
@exbolitadequeso (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 11, 2020
24
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Michael Nolan
@Mick__geek (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 11, 2020
24
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Matteo Manica
@drugilsberg (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 11, 2020
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J. M. Nápoles
@napoles3D (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 11, 2020
24
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Mario Krenn
@MarioKrenn6240 (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 11, 2020
24
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Gabriel Merino
@theochemmerida (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 11, 2020
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김재욱,Jaewook Kim
@tigger1o (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 11, 2020
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johnd
@jmdagdelen (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 11, 2020
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Caroline Lynn Kamerlin
@kamerlinlab (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 11, 2020
24
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ACS Central Science
@ACSCentSci (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 11, 2020
24
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The Machine Learning Bot
@botnowa (Twitter)RT @A_Aspuru_Guzik: Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevan…
view full postSeptember 11, 2020
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Alan Aspuru-Guzik
@A_Aspuru_Guzik (Twitter)Super excited to share our @ACSCentSci paper on #AI for Computer Vision for Chemical applications. This is very relevant for automating chemistry
view full postSeptember 11, 2020
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ChemRxiv
@ChemRxiv (Twitter)Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset by Alan Aspuru-Guzik & co-workers https://t.co/78ha8lZB0p https://t.co/8qVvMbDhOT
view full postMarch 4, 2020
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Abstract Synopsis
- This work introduces a machine learning approach for recognizing materials and vessels in chemistry labs using computer vision, supported by a new dataset called VectorLabPics, which contains 2187 annotated images of transparent containers and their contents.
- The dataset includes detailed annotations for vessels, materials (liquids, solids, foams, suspensions, powders), fill levels, corks, and vessel parts, enabling the training of neural networks for tasks like segmenting and classifying these components.
- The trained models performed well in detecting and classifying vessels and individual material phases, but showed lower accuracy when segmenting complex multiphase systems such as phase-separating liquids.]
RealTimeChem (no longer active)
@RealTimeChem (Twitter)