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pascalmusabyimana

pascal-maker

https://pascal-maker.github.io/developedbypascalmusabyimana/

AI & ML interests

computer vision, nlp , machine learning and deeplearning

Recent Activity

reacted to branikita's post with 🚀 about 4 hours ago

With https://huggingface.co/Robonine team we recently verified the rotational speed of the Feetech STS3250 servo motor (12 V, 50 kg·cm torque, magnetic encoder) to compare measured performance with the official specification. According to the datasheet: - Rated speed: 0.133 s per 60° Calculation: - 0.133 s × 6 = 0.798 s per full rotation - 1 / 0.798 = 1.253 revolutions per second - 1.253 × 60 = 75.2 RPM This confirms the official specification of approximately 75 RPM at 12 V under no load. Our measurement: - Encoder output: 5,300 values per second - Encoder resolution: 4,096 counts per revolution Calculation: - Revolutions per second = 5,300 ÷ 4,096 = 1.294 rev/s - RPM = 1.294 × 60 = 77.6 RPM Result: The measured value differs by only about 2–3% from the datasheet specification, confirming that the STS3250 performs very close to its rated no-load speed. This close agreement validates both the servo’s performance and our measurement approach. Video: https://youtube.com/shorts/_O_mVZvYQlQ?feature=share #servo #robotics #automation #motioncontrol #mechatronics #Feetech #STS3250 #engineering #datasheet #precision #hardwaretesting #roboticsengineering

reacted to DmitryRyumin's post with 🔥 about 4 hours ago

🚀💡🌟 New Research Alert - ICCV 2025 (Oral)! 🌟🪄🚀 📄 Title: LoftUp: Learning a Coordinate-based Feature Upsampler for Vision Foundation Models 🔝 📝 Description: LoftUp is a coordinate-based transformer that upscales the low-resolution features of VFMs (e.g. DINOv2 and CLIP) using cross-attention and self-distilled pseudo-ground truth (pseudo-GT) from SAM. 👥 Authors: Haiwen Huang, Anpei Chen, Volodymyr Havrylov, Andreas Geiger, and Dan Zhang 📅 Conference: ICCV, 19 – 23 Oct, 2025 | Honolulu, Hawai'i, USA 🇺🇸 📄 Paper: https://huggingface.co/papers/2504.14032 🌐 Github Page: https://andrehuang.github.io/loftup-site 📁 Repository: https://github.com/andrehuang/loftup 🚀 ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers 🚀 Added to the Foundation Models and Representation Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/foundation-models-and-representation-learning.md 📚 More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin 🔍 Keywords: #LoftUp #VisionFoundationModels #FeatureUpsampling #Cross-AttentionTransformer #CoordinateBasedLearning #SelfDistillation #PseudoGroundTruth #RepresentationLearning #AI #ICCV2025 #ResearchHighlight

reacted to DmitryRyumin's post with 🔥 about 10 hours ago

🚀🏷️🌟 New Research Alert - ICCV 2025 (Oral)! 🌟🧩🚀 📄 Title: Heavy Labels Out! Dataset Distillation with Label Space Lightening 🔝 📝 Description: The HeLlO framework is a new corpus distillation method that removes the need for large soft labels. It uses a lightweight, online image-to-label projector based on CLIP. This projector has been adapted using LoRA-style, parameter-efficient tuning. It has also been initialized with text embeddings. 👥 Authors: @roseannelexie, @Huage001, Zigeng Chen, Jingwen Ye, and Xinchao Wang 📅 Conference: ICCV, 19 – 23 Oct, 2025 | Honolulu, Hawai'i, USA 🇺🇸 📄 Paper: https://huggingface.co/papers/2408.08201 📺 Video: https://www.youtube.com/watch?v=kAyK_3wskgA 🚀 ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers 🚀 Added to the Efficient Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/efficient-learning.md 📚 More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin 🔍 Keywords: #DatasetDistillation #LabelCompression #CLIP #LoRA #EfficientAI #FoundationModels #AI #ICCV2025 #ResearchHighlight

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reacted to branikita's post with 🚀 about 4 hours ago

Post

1754

With

Robonine team we recently verified the rotational speed of the Feetech STS3250 servo motor (12 V, 50 kg·cm torque, magnetic encoder) to compare measured performance with the official specification.

According to the datasheet:
- Rated speed: 0.133 s per 60°

Calculation:
- 0.133 s × 6 = 0.798 s per full rotation
- 1 / 0.798 = 1.253 revolutions per second
- 1.253 × 60 = 75.2 RPM

This confirms the official specification of approximately 75 RPM at 12 V under no load.

Our measurement:
- Encoder output: 5,300 values per second
- Encoder resolution: 4,096 counts per revolution

Calculation:
- Revolutions per second = 5,300 ÷ 4,096 = 1.294 rev/s
- RPM = 1.294 × 60 = 77.6 RPM

Result:
The measured value differs by only about 2–3% from the datasheet specification, confirming that the STS3250 performs very close to its rated no-load speed. This close agreement validates both the servo’s performance and our measurement approach.

Video: https://youtube.com/shorts/_O_mVZvYQlQ?feature=share

#servo #robotics #automation #motioncontrol #mechatronics #Feetech #STS3250 #engineering #datasheet #precision #hardwaretesting #roboticsengineering

reacted to DmitryRyumin's post with 🔥 about 4 hours ago

Post

2011

🚀💡🌟 New Research Alert - ICCV 2025 (Oral)! 🌟🪄🚀
📄 Title: LoftUp: Learning a Coordinate-based Feature Upsampler for Vision Foundation Models 🔝

📝 Description: LoftUp is a coordinate-based transformer that upscales the low-resolution features of VFMs (e.g. DINOv2 and CLIP) using cross-attention and self-distilled pseudo-ground truth (pseudo-GT) from SAM.

👥 Authors: Haiwen Huang, Anpei Chen, Volodymyr Havrylov, Andreas Geiger, and Dan Zhang

📅 Conference: ICCV, 19 – 23 Oct, 2025 | Honolulu, Hawai'i, USA 🇺🇸

📄 Paper: LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models (2504.14032)

🌐 Github Page: https://andrehuang.github.io/loftup-site
📁 Repository: https://github.com/andrehuang/loftup

🚀 ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers

🚀 Added to the Foundation Models and Representation Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/foundation-models-and-representation-learning.md

📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🔍 Keywords: #LoftUp #VisionFoundationModels #FeatureUpsampling #Cross-AttentionTransformer #CoordinateBasedLearning #SelfDistillation #PseudoGroundTruth #RepresentationLearning #AI #ICCV2025 #ResearchHighlight

reacted to DmitryRyumin's post with 🔥 about 10 hours ago

Post

1820

🚀🏷️🌟 New Research Alert - ICCV 2025 (Oral)! 🌟🧩🚀
📄 Title: Heavy Labels Out! Dataset Distillation with Label Space Lightening 🔝

📝 Description: The HeLlO framework is a new corpus distillation method that removes the need for large soft labels. It uses a lightweight, online image-to-label projector based on CLIP. This projector has been adapted using LoRA-style, parameter-efficient tuning. It has also been initialized with text embeddings.

👥 Authors: @roseannelexie , @Huage001 , Zigeng Chen, Jingwen Ye, and Xinchao Wang

📅 Conference: ICCV, 19 – 23 Oct, 2025 | Honolulu, Hawai'i, USA 🇺🇸

📄 Paper: Heavy Labels Out! Dataset Distillation with Label Space Lightening (2408.08201)

📺 Video: https://www.youtube.com/watch?v=kAyK_3wskgA

🚀 ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers

🚀 Added to the Efficient Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/efficient-learning.md

📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🔍 Keywords: #DatasetDistillation #LabelCompression #CLIP #LoRA #EfficientAI #FoundationModels #AI #ICCV2025 #ResearchHighlight

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🤗 Sentence Transformers is joining Hugging Face! 🤗 This formalizes the existing maintenance structure, as I've personally led the project for the past two years on behalf of Hugging Face! Details:

Today, the Ubiquitous Knowledge Processing (UKP) Lab is transferring the project to Hugging Face. Sentence Transformers will remain a community-driven, open-source project, with the same open-source license (Apache 2.0) as before. Contributions from researchers, developers, and enthusiasts are welcome and encouraged. The project will continue to prioritize transparency, collaboration, and broad accessibility.

Read our full announcement for more details and quotes from UKP and Hugging Face leadership: https://huggingface.co/blog/sentence-transformers-joins-hf

We see an increasing wish from companies to move from large LLM APIs to local models for better control and privacy, reflected in the library's growth: in just the last 30 days, Sentence Transformer models have been downloaded >270 million times, second only to transformers.

I would like to thank the UKP Lab, and especially Nils Reimers and Iryna Gurevych, both for their dedication to the project and for their trust in myself, both now and two years ago. Back then, neither of you knew me well, yet you trusted me to take the project to new heights. That choice ended up being very valuable for the embedding & Information Retrieval community, and I think this choice of granting Hugging Face stewardship will be similarly successful.

I'm very excited about the future of the project, and for the world of embeddings and retrieval at large!