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AI synopsis
The collection highlights widespread support and enthusiasm, exemplified by comments praising user guides, tutorials, and the active user community, with words like 'great', 'active', and 'possible' emphasizing both interest and perceived impact. The tone suggests that the publication is seen as a valuable tool advancing behavioral analysis and digital tracking technologies.
Most discussions express positive approval and support for the publication, with notes on its usefulness and community engagement.
The topic garners high interest among users, with mentions of new features, community guides, and technological applications.
Discussions show active participation, sharing resources like tutorials, packages, and questions about implementation.
The posts suggest that the publication is viewed as influential in the research community, promoting the adoption of markerless pose estimation methods.
Always great to see nice user-based guides! A great complementary guide to our growing docs and YouTube videos ( https://t.co/Ys9BaRtopK) thanks @G_HidalgoGadea
#DeepLabCut はてなブログに投稿しました 【論文読み】DeepLabCut: markerless pose estimation of user-defined body parts with deep learning - 反面教師あり学習 https://t.co/WyRxJbRQcB #はてなブログ
@MorphoFun @mechsNmorph @mrmilesvalencia @GlennaClifton There is a very active user community: https://t.co/e2Il9Qeodr and detailed full use-guide: https://t.co/HdC8AfVtDZ plus YouTube Tutorials: https://t.co/0Zgt4sfaBC
@BohacekLab suppl. here: https://t.co/fIaCeZIDlB
@dineshraov No, the Docker is just for training and inference, so I install the simple cpu only version of dlc with conda, see how I use both here: https://t.co/IvFwE91LEA
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning via @sanxiyn https://t.co/DemIBhZkpu #ai #science https://t.co/c9xiw7X4Yw
This demo shows how to use DeepLabCut in ipython within an Anaconda environment and train the network in Docker, enabling markerless tracking of animal and human body parts with minimal training data.
This video presents a new method for tracking animal and human body parts without physical markers, utilizing deep learning and transfer learning for high accuracy with minimal training data. It offers faster, less…
DeepLabCut enables tracking of animal and human body parts without physical markers, utilizing deep learning and transfer learning for high accuracy with minimal data. This method facilitates faster, less invasive…
DeepLabCut utilizes deep learning for markerless tracking of animal and human body parts, reducing the need for physical markers, enabling fast and accurate behavior analysis across species with minimal training data.
This video demonstrates DeepLabCut, a markerless pose estimation method that tracks Drosophila egg-laying behavior with minimal training data, across cluttered backgrounds, enabling fast, accurate, and non-invasive…
This video compares DeepLabCut and SLEAP, exploring their evolution and integration for behavior identification. It highlights how deep learning improves animal pose estimation, enabling quick, accurate tracking across…
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning https://t.co/ABPdLoSqlv
RT @DeepLabCut: Always great to see nice user-based guides! A great complementary guide to our growing docs and YouTube videos ( https://t.…
Always great to see nice user-based guides! A great complementary guide to our growing docs and YouTube videos ( https://t.co/Ys9BaRtopK) thanks @G_HidalgoGadea
@tibor_magura It's great to see that it's possible to do video tracking in R! A couple of years ago one of the students I co-supervised used DeepLabCut (https://t.co/2lodMJhyue) for something similar but R is simply R :) May I know which package you use?
Aquí un video de cómo una mosquita de la fruta pone un huevo… huevo ingrato que eventualmente se deshará de la herencia materna que ya no necesite. Video de Mackenzie Mathis et al. (no relacionado con la investigación reciente de la enzima Kdo). 4/4 https://t.co/BhUkvpnDRD
@BohacekLab suppl. here: https://t.co/fIaCeZIDlB
@dineshraov No, the Docker is just for training and inference, so I install the simple cpu only version of dlc with conda, see how I use both here: https://t.co/IvFwE91LEA
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning via @sanxiyn https://t.co/DemIBhZkpu #ai #science https://t.co/c9xiw7X4Yw
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning:https://t.co/Sb1jwP5LG3
@MorphoFun @mechsNmorph @mrmilesvalencia @GlennaClifton There is a very active user community: https://t.co/e2Il9Qeodr and detailed full use-guide: https://t.co/HdC8AfVtDZ plus YouTube Tutorials: https://t.co/0Zgt4sfaBC
RT @eqseqseqs: #DeepLabCut はてなブログに投稿しました 【論文読み】DeepLabCut: markerless pose estimation of user-defined body parts with deep learning - 反面教師あ…
RT @eqseqseqs: #DeepLabCut はてなブログに投稿しました 【論文読み】DeepLabCut: markerless pose estimation of user-defined body parts with deep learning - 反面教師あ…
#DeepLabCut はてなブログに投稿しました 【論文読み】DeepLabCut: markerless pose estimation of user-defined body parts with deep learning - 反面教師あり学習 https://t.co/WyRxJbRQcB #はてなブログ