#segmentanything — Public Fediverse posts

Meta’s SAM 3 is the new AI superpower for visuals. Find & select anything in photos/videos, just by typing, clicking, or dropping an example. No more tedious edits, just instant results.

Check out my fun explainer here:
https://techglimmer.io/meta-segment-anything-model-3-sam3-explained/

#Meta #AI #SegmentAnything #Sam3 #PhotoEditing

Meta’s SAM 3 is the new AI superpower for visuals. Find & select anything in photos/videos, just by typing, clicking, or dropping an example. No more tedious edits, just instant results.

Check out my fun explainer here:
https://techglimmer.io/meta-segment-anything-model-3-sam3-explained/

#Meta #AI #SegmentAnything #Sam3 #PhotoEditing

Meta’s SAM 3 is the new AI superpower for visuals. Find & select anything in photos/videos, just by typing, clicking, or dropping an example. No more tedious edits, just instant results.

Check out my fun explainer here:
https://techglimmer.io/meta-segment-anything-model-3-sam3-explained/

#Meta #AI #SegmentAnything #Sam3 #PhotoEditing

Meta’s SAM 3 is the new AI superpower for visuals. Find & select anything in photos/videos, just by typing, clicking, or dropping an example. No more tedious edits, just instant results.

Check out my fun explainer here:
https://techglimmer.io/meta-segment-anything-model-3-sam3-explained/

#Meta #AI #SegmentAnything #Sam3 #PhotoEditing

Meta’s SAM 3 is the new AI superpower for visuals. Find & select anything in photos/videos, just by typing, clicking, or dropping an example. No more tedious edits, just instant results.

Check out my fun explainer here:
https://techglimmer.io/meta-segment-anything-model-3-sam3-explained/

#Meta #AI #SegmentAnything #Sam3 #PhotoEditing

Meta’s latest SAM 3 model shows promise but stumbles on niche technical terms and complex logical prompts. While its zero‑shot abilities shine on general images, medical‑imaging tasks and 3‑D segmentation still lag behind Llama and Gemini. Find out what this means for open‑source vision research and where the community can help improve it. #MetaSAM3 #SegmentAnything #ZeroShotAI #MedicalImaging

🔗 https://aidailypost.com/news/metas-sam-3-falters-niche-technical-terms-complex-logical-prompts

Meta’s latest SAM 3 model shows promise but stumbles on niche technical terms and complex logical prompts. While its zero‑shot abilities shine on general images, medical‑imaging tasks and 3‑D segmentation still lag behind Llama and Gemini. Find out what this means for open‑source vision research and where the community can help improve it. #MetaSAM3 #SegmentAnything #ZeroShotAI #MedicalImaging

🔗 https://aidailypost.com/news/metas-sam-3-falters-niche-technical-terms-complex-logical-prompts

Meta ra mắt SAM 3: Công nghệ phân đoạn hình ảnh với concept.
#SAM3 #Meta #TríTuệNhânTạo #AI #SegmentAnything
#CôngNghệMới #TríTuệArtificial #MetaSuperintelligenceLabs

https://www.reddit.com/r/LocalLLaMA/comments/1p1df5y/sam_3_segment_anything_with_concepts_by_meta/

I'd love to work on shows where SAM2 is good enough. But we have actors with fine hair detail and the comps need to survive 4K inspection so.... Indian roto vendors it is :-/

https://floss.social/@kdenlive/114416926889657357

There's a #SegmentAnything toolset for #Nuke: https://github.com/Theo-SAMINADIN-td/NukeSamurai

It works but the lack of temporal stability makes it break down when actors are not moving. All the cool test footage is people running or objects moving around which is much more forgiving.

I'd love to work on shows where SAM2 is good enough. But we have actors with fine hair detail and the comps need to survive 4K inspection so.... Indian roto vendors it is :-/

https://floss.social/@kdenlive/114416926889657357

There's a #SegmentAnything toolset for #Nuke: https://github.com/Theo-SAMINADIN-td/NukeSamurai

It works but the lack of temporal stability makes it break down when actors are not moving. All the cool test footage is people running or objects moving around which is much more forgiving.

I'd love to work on shows where SAM2 is good enough. But we have actors with fine hair detail and the comps need to survive 4K inspection so.... Indian roto vendors it is :-/

https://floss.social/@kdenlive/114416926889657357

There's a #SegmentAnything toolset for #Nuke: https://github.com/Theo-SAMINADIN-td/NukeSamurai

It works but the lack of temporal stability makes it break down when actors are not moving. All the cool test footage is people running or objects moving around which is much more forgiving.

I'd love to work on shows where SAM2 is good enough. But we have actors with fine hair detail and the comps need to survive 4K inspection so.... Indian roto vendors it is :-/

https://floss.social/@kdenlive/114416926889657357

There's a #SegmentAnything toolset for #Nuke: https://github.com/Theo-SAMINADIN-td/NukeSamurai

It works but the lack of temporal stability makes it break down when actors are not moving. All the cool test footage is people running or objects moving around which is much more forgiving.

I'd love to work on shows where SAM2 is good enough. But we have actors with fine hair detail and the comps need to survive 4K inspection so.... Indian roto vendors it is :-/

https://floss.social/@kdenlive/114416926889657357

There's a #SegmentAnything toolset for #Nuke: https://github.com/Theo-SAMINADIN-td/NukeSamurai

It works but the lack of temporal stability makes it break down when actors are not moving. All the cool test footage is people running or objects moving around which is much more forgiving.

The recording of my presentation at the TensorFlow Working Group at SERVIR

Title: Automated Segmentation of Remote Sensing Imagery with the Segment Anything Model
Video: https://www.youtube.com/watch?v=45NpHeq1X6I
Slides: https://bit.ly/TFWG
GitHub: https://github.com/opengeos/segment-geospatial

#segmentanything #geospatial #opensource #SERVIR

I am honestly floored at the #SegmentAnything implementation for #ImageJ / #Fiji

Even running on a laptop, once loaded, it's incredibly quick.

Moreover, it's a super-simple install which is a major barrier to many #AI #DeepLearning implementations.

Time to play around with some #Microscopy and #DigitalPathology data!

Details here: https://github.com/segment-anything-models-java/SAMJ-IJ
Photo source: https://www.pexels.com/photo/photo-of-railway-on-mountain-near-houses-1658967/

#segmentanything #Cloisonismus

#segmentanything #Cloisonismus

#segmentanything #Cloisonismus

#segmentanything #Cloisonismus

#segmentanything #Cloisonismus

I am honestly floored at the #SegmentAnything implementation for #ImageJ / #Fiji

Even running on a laptop, once loaded, it's incredibly quick.

Moreover, it's a super-simple install which is a major barrier to many #AI #DeepLearning implementations.

Time to play around with some #Microscopy and #DigitalPathology data!

Details here: https://github.com/segment-anything-models-java/SAMJ-IJ
Photo source: https://www.pexels.com/photo/photo-of-railway-on-mountain-near-houses-1658967/

I am honestly floored at the #SegmentAnything implementation for #ImageJ / #Fiji

Even running on a laptop, once loaded, it's incredibly quick.

Moreover, it's a super-simple install which is a major barrier to many #AI #DeepLearning implementations.

Time to play around with some #Microscopy and #DigitalPathology data!

Details here: https://github.com/segment-anything-models-java/SAMJ-IJ
Photo source: https://www.pexels.com/photo/photo-of-railway-on-mountain-near-houses-1658967/

I am honestly floored at the #SegmentAnything implementation for #ImageJ / #Fiji

Even running on a laptop, once loaded, it's incredibly quick.

Moreover, it's a super-simple install which is a major barrier to many #AI #DeepLearning implementations.

Time to play around with some #Microscopy and #DigitalPathology data!

Details here: https://github.com/segment-anything-models-java/SAMJ-IJ
Photo source: https://www.pexels.com/photo/photo-of-railway-on-mountain-near-houses-1658967/

I am honestly floored at the #SegmentAnything implementation for #ImageJ / #Fiji

Even running on a laptop, once loaded, it's incredibly quick.

Moreover, it's a super-simple install which is a major barrier to many #AI #DeepLearning implementations.

Time to play around with some #Microscopy and #DigitalPathology data!

Details here: https://github.com/segment-anything-models-java/SAMJ-IJ
Photo source: https://www.pexels.com/photo/photo-of-railway-on-mountain-near-houses-1658967/

"Segment Anything as a Service" shared our approach to making advanced AI tools accessible and enhancing workflows cost-effectively with the Segment Anything Model. #segmentanything #AI #machinelearning #geospatial
🔗 https://developmentseed.org/blog/2023-06-08-segment-anything-services

"Segment Anything as a Service" shared our approach to making advanced AI tools accessible and enhancing workflows cost-effectively with the Segment Anything Model. #segmentanything #AI #machinelearning #geospatial
🔗 https://developmentseed.org/blog/2023-06-08-segment-anything-services

"Segment Anything as a Service" shared our approach to making advanced AI tools accessible and enhancing workflows cost-effectively with the Segment Anything Model. #segmentanything #AI #machinelearning #geospatial
🔗 https://developmentseed.org/blog/2023-06-08-segment-anything-services

"Segment Anything as a Service" shared our approach to making advanced AI tools accessible and enhancing workflows cost-effectively with the Segment Anything Model. #segmentanything #AI #machinelearning #geospatial
🔗 https://developmentseed.org/blog/2023-06-08-segment-anything-services

"Segment Anything as a Service" shared our approach to making advanced AI tools accessible and enhancing workflows cost-effectively with the Segment Anything Model. #segmentanything #AI #machinelearning #geospatial
🔗 https://developmentseed.org/blog/2023-06-08-segment-anything-services

"Exploring the Potential of the Segment Anything Model" highlighted innovative strides in AI, pushing boundaries in data annotation and satellite imagery. #segmentanything #machinelearning #computervision
🔗 https://developmentseed.org/blog/2023-05-19-segment-anything-potential

"Exploring the Potential of the Segment Anything Model" highlighted innovative strides in AI, pushing boundaries in data annotation and satellite imagery. #segmentanything #machinelearning #computervision
🔗 https://developmentseed.org/blog/2023-05-19-segment-anything-potential

"Exploring the Potential of the Segment Anything Model" highlighted innovative strides in AI, pushing boundaries in data annotation and satellite imagery. #segmentanything #machinelearning #computervision
🔗 https://developmentseed.org/blog/2023-05-19-segment-anything-potential

"Exploring the Potential of the Segment Anything Model" highlighted innovative strides in AI, pushing boundaries in data annotation and satellite imagery. #segmentanything #machinelearning #computervision
🔗 https://developmentseed.org/blog/2023-05-19-segment-anything-potential

"Exploring the Potential of the Segment Anything Model" highlighted innovative strides in AI, pushing boundaries in data annotation and satellite imagery. #segmentanything #machinelearning #computervision
🔗 https://developmentseed.org/blog/2023-05-19-segment-anything-potential

Video series and article coming soon leveraging the brilliant Segment Geospatial to create a workflow for effectively creating mask datasets for training a U-Net model #SegmentAnything #Meta #GIS #GISChat #technology

I'm proud to have published my first scientific paper as preprint on arXiv! https://arxiv.org/abs/2310.08683

I investigated the use of augmented reality for reinforcement learning agents by integrating Meta Research's SAM (Segment Anything) zero-shot model into the RL training data pipeline. Like equipping a virtual entity (the RL agent) with AR goggles.

#datascience #machinelearning #artificialintelligence #augmentedreality #reinforcementlearning #arxiv #atari #videogame #metaresearch #segmentanything

I'm proud to have published my first scientific paper as preprint on arXiv! https://arxiv.org/abs/2310.08683

I investigated the use of augmented reality for reinforcement learning agents by integrating Meta Research's SAM (Segment Anything) zero-shot model into the RL training data pipeline. Like equipping a virtual entity (the RL agent) with AR goggles.

#datascience #machinelearning #artificialintelligence #augmentedreality #reinforcementlearning #arxiv #atari #videogame #metaresearch #segmentanything

I'm proud to have published my first scientific paper as preprint on arXiv! https://arxiv.org/abs/2310.08683

I investigated the use of augmented reality for reinforcement learning agents by integrating Meta Research's SAM (Segment Anything) zero-shot model into the RL training data pipeline. Like equipping a virtual entity (the RL agent) with AR goggles.

#datascience #machinelearning #artificialintelligence #augmentedreality #reinforcementlearning #arxiv #atari #videogame #metaresearch #segmentanything

I'm proud to have published my first scientific paper as preprint on arXiv! https://arxiv.org/abs/2310.08683

I investigated the use of augmented reality for reinforcement learning agents by integrating Meta Research's SAM (Segment Anything) zero-shot model into the RL training data pipeline. Like equipping a virtual entity (the RL agent) with AR goggles.

#datascience #machinelearning #artificialintelligence #augmentedreality #reinforcementlearning #arxiv #atari #videogame #metaresearch #segmentanything

I'm proud to have published my first scientific paper as preprint on arXiv! https://arxiv.org/abs/2310.08683

I investigated the use of augmented reality for reinforcement learning agents by integrating Meta Research's SAM (Segment Anything) zero-shot model into the RL training data pipeline. Like equipping a virtual entity (the RL agent) with AR goggles.

#datascience #machinelearning #artificialintelligence #augmentedreality #reinforcementlearning #arxiv #atari #videogame #metaresearch #segmentanything

Segment-geospatial v0.9.1 is out. It now supports segmenting remote sensing imagery with the High-Quality Segment Anything Model (HQ-SAM)

Video: https://youtu.be/n-FZzKirE9I
Notebook: https://samgeo.gishub.org/examples/input_prompts_hq
GitHub: https://github.com/opengeos/segment-geospatial

#segmentanything #geospatial #deeplearning

Segment-geospatial v0.9.1 is out. It now supports segmenting remote sensing imagery with the High-Quality Segment Anything Model (HQ-SAM)

Video: https://youtu.be/n-FZzKirE9I
Notebook: https://samgeo.gishub.org/examples/input_prompts_hq
GitHub: https://github.com/opengeos/segment-geospatial

#segmentanything #geospatial #deeplearning

Segment-geospatial v0.9.1 is out. It now supports segmenting remote sensing imagery with the High-Quality Segment Anything Model (HQ-SAM)

Video: https://youtu.be/n-FZzKirE9I
Notebook: https://samgeo.gishub.org/examples/input_prompts_hq
GitHub: https://github.com/opengeos/segment-geospatial

#segmentanything #geospatial #deeplearning

Segment-geospatial v0.9.1 is out. It now supports segmenting remote sensing imagery with the High-Quality Segment Anything Model (HQ-SAM)

Video: https://youtu.be/n-FZzKirE9I
Notebook: https://samgeo.gishub.org/examples/input_prompts_hq
GitHub: https://github.com/opengeos/segment-geospatial

#segmentanything #geospatial #deeplearning

Segment-geospatial v0.9.1 is out. It now supports segmenting remote sensing imagery with the High-Quality Segment Anything Model (HQ-SAM)

Video: https://youtu.be/n-FZzKirE9I
Notebook: https://samgeo.gishub.org/examples/input_prompts_hq
GitHub: https://github.com/opengeos/segment-geospatial

#segmentanything #geospatial #deeplearning

Does anyone know of open source projects that use AI to segment orthorectified imagery and try to categorise bicycle paths or other cycling infrastructure, and check if they’re missing from OpenStreetMap?

I’m thinking of whether it would be possible to run Meta’s Segment Anything model over suitably licensed aerial imagery to find infra missing on OSM. Any thoughts/comments very welcome!

#openstreetmap #meta #segmentanything #ai #computervision #geospatial #gis #cycling #bikes #bicycles

Does anyone know of open source projects that use AI to segment orthorectified imagery and try to categorise bicycle paths or other cycling infrastructure, and check if they’re missing from OpenStreetMap?

I’m thinking of whether it would be possible to run Meta’s Segment Anything model over suitably licensed aerial imagery to find infra missing on OSM. Any thoughts/comments very welcome!

#openstreetmap #meta #segmentanything #ai #computervision #geospatial #gis #cycling #bikes #bicycles

Does anyone know of open source projects that use AI to segment orthorectified imagery and try to categorise bicycle paths or other cycling infrastructure, and check if they’re missing from OpenStreetMap?

I’m thinking of whether it would be possible to run Meta’s Segment Anything model over suitably licensed aerial imagery to find infra missing on OSM. Any thoughts/comments very welcome!

#openstreetmap #meta #segmentanything #ai #computervision #geospatial #gis #cycling #bikes #bicycles

Does anyone know of open source projects that use AI to segment orthorectified imagery and try to categorise bicycle paths or other cycling infrastructure, and check if they’re missing from OpenStreetMap?

I’m thinking of whether it would be possible to run Meta’s Segment Anything model over suitably licensed aerial imagery to find infra missing on OSM. Any thoughts/comments very welcome!

#openstreetmap #meta #segmentanything #ai #computervision #geospatial #gis #cycling #bikes #bicycles

Does anyone know of open source projects that use AI to segment orthorectified imagery and try to categorise bicycle paths or other cycling infrastructure, and check if they’re missing from OpenStreetMap?

I’m thinking of whether it would be possible to run Meta’s Segment Anything model over suitably licensed aerial imagery to find infra missing on OSM. Any thoughts/comments very welcome!

#openstreetmap #meta #segmentanything #ai #computervision #geospatial #gis #cycling #bikes #bicycles