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ndaheim authored a paper about 2 months ago

Parameter Exploration for RLVR via Variational Learning

j-chim authored a paper 3 months ago

The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset

j-chim authored a paper 3 months ago

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

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dronefreak

posted an update 2 days ago

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🚀 Excited to open-source the Brackish Underwater Object Detection Model Zoo on Hugging Face.

This release includes:

- 🤖 8 YOLOv8, YOLOv11, and YOLOv26 object detection models trained on Brackish, spanning nano through x-large variants (RF-DETR variants to follow once I have the compute for them).
- 🐟 Benchmarked on Brackish's underwater marine-animal detection task — crab, fish, jellyfish, shrimp, small_fish, and starfish, captured 9 meters below the surface under naturally varying visibility conditions.
- 📊 Detailed model cards with mAP/precision/recall, per-class breakdowns, PR/F1 curves and confusion matrices, qualitative detection showcases, and full training configurations for reproducibility.

Headline numbers:
- 🏆 Best mAP@50: 99.3% (YOLOv8s), 85.65% mAP@50:95, 99.36% precision.
- ⚡ Best efficiency tradeoff: YOLOv26n hits 98.95% mAP@50 at just 6.1 GFLOPs (2.6M params) — within 0.35 points of the top model while using ~4.7x fewer FLOPs (28.6 GFLOPs). The whole zoo actually clusters tightly here (98.74–99.3% mAP@50 across all 8 models), so there's little accuracy left on the table by going small on this dataset.

If you're working on underwater perception, marine biology/ecology monitoring, or robotics in low-visibility environments, I hope these resources are useful.

📦 Dataset:
dronefreak/Brackish

🤖 Model Collection: dronefreak/brackish-underwater-object-detection-model-zoo-6a7c153687b2821da0c6591d

Feedback, bug reports, and contributions are always welcome.

dronefreak

posted an update 7 days ago

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🚀 Open-sourcing the KITTI Object Detection Model Zoo on Hugging Face.

- 🤖 12 models: YOLOv8, YOLOv9, YOLO11 and YOLO26, from nano/tiny up to x-large.
- 🚗 Street-scene detection: cars, cyclists, pedestrians, vans, trucks and more, in KITTI's ultra-wide frames.
- 📊 Model cards with metrics, per-class results, curves, showcases, a demo video and full configs.

Headline numbers:
- 🏆 Best mAP@50: 43.73% (YOLO26x). Best mAP@50:95: 26.54% (YOLO26s).
- ⚡ YOLO26n gets 42.54% mAP@50 at just 6.1 GFLOPs, within 1.2 points of YOLO26x at ~34x fewer FLOPs.

Trained and evaluated with DetectionBench: https://github.com/dronefreak/DetectionBench

Dataset credit: Andreas Geiger, Philip Lenz and Raquel Urtasun (CVPR 2012). This is an unofficial YOLO-ready reformat (CC BY-NC-SA 3.0). Metrics are on the validation split, since KITTI has no public test labels.

📦 Dataset: dronefreak/KITTI
🤖 Collection: dronefreak/kitti-object-detection-model-zoo-6ab4cb94fb08dccf3bde2e8b

Feedback and contributions welcome.

dronefreak

posted an update 8 days ago

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🚀 Open-sourcing the BDD100K Object Detection Model Zoo on Hugging Face.

- 🤖 10 models: YOLOv8/v9/v10/YOLO11/YOLO26 (n/s) and RF-DETR Nano.
- 🚗 Driving-scene detection: 10 classes including traffic lights and signs, across varied weather and lighting.
- 📊 Model cards with metrics, per-class results, curves, showcases, dashcam demo videos and full configs.

Headline numbers:
- 🏆 Best mAP@50: 58.76% (YOLO26s), 33.86% mAP@50:95.
- ⚡ YOLO26n gets 52.25% mAP@50 at 6.1 GFLOPs, ~3.7x fewer than YOLO26s.

Trained and evaluated with DetectionBench: https://github.com/dronefreak/DetectionBench

Dataset credit: Fisher Yu et al. (UC Berkeley, CVPR 2020). It has a non-commercial license, so it is not mirrored; get it from https://www.bdd100k.com/. The "test" split here is BDD100K's official validation set.

🤖 Collection: dronefreak/bdd100k-object-detection-model-zoo-6aafc46f2f6c5e4d8676d894

Feedback and contributions welcome.

dronefreak

posted an update 9 days ago

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🚀 Open-sourcing the UAVDT Object Detection Model Zoo on Hugging Face.

- 🤖 18 models: YOLOv8/v9/v10/YOLO11/YOLO26 (n–m) and RF-DETR Nano/Small/Medium.
- 🚁 Drone traffic surveillance: cars, trucks and buses, mostly tiny objects (median box ~0.14% of the image).
- 📊 Model cards with metrics, per-class results, curves, showcases, demo videos and full configs.

Headline numbers:
- 🏆 Best mAP@50: 33.43% (YOLOv26m). Best mAP@50:95: 20.54% (RF-DETR Medium).
- ⚡ YOLOv26s gets 32.98% mAP@50 at 22.8 GFLOPs, ~3.3x fewer than YOLOv26m.

Trained and evaluated with DetectionBench: https://github.com/dronefreak/DetectionBench

Dataset credit: Dawei Du et al. (ECCV 2018). It is research-only, so I don't mirror it; the dataset repo is a guide to the official source.

📦 Dataset: dronefreak/UAVDT
🤖 Collection: dronefreak/uavdt-object-detection-model-zoo-6aadb3673084702d3ed45ff0

Feedback and contributions welcome.

dronefreak

posted an update about 2 months ago

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🌧️❄️ Free demo: remove rain, raindrops, or snow from a photo with a single model

I put together an unofficial demo for **Histoformer** (ECCV 2024, arXiv: 2407.10172), a 16.6M-parameter transformer that handles three different weather degradations, rain streaks, adherent raindrops, and snow, in one unified model. It uses a "histogram self-attention" mechanism that groups pixels by degradation intensity instead of spatial position, which is a
neat way to sidestep the usual spatial-window tradeoffs in restoration transformers.

Try it here, free on ZeroGPU: dronefreak/histoformer-weather-restoration

Upload a photo and get a before/after slider. Two checkpoints available: one tuned for real-world photos, one for the paper's synthetic benchmarks.

Also put together a cleaner, easy-to-use model card with a copy-pasteable Quickstart if you'd rather run it yourself: dronefreak/Histoformer

This is an unofficial demo/mirror, not affiliated with the original authors. All credit for the actual research goes to Shangquan Sun, Wenqi Ren, Xinwei Gao, Rui Wang, and Xiaochun Cao (@sunsean ). Official repo: https://github.com/sunshangquan/Histoformer. Weights are MIT-licensed.

Reported numbers from the paper: 32.1 PSNR on rain+fog (Outdoor-Rain), 33.1 on raindrops, 37.4 / 32.2 on light/heavy snow (Snow100K-S/L).

2 replies

ndaheim

authored a paper about 2 months ago

Parameter Exploration for RLVR via Variational Learning

Paper • 2608.09805 • Published Aug 10 • 8

dronefreak

posted an update about 2 months ago

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🚀 Excited to open-source the SeaDronesSee Object Detection Model Zoo on Hugging Face.

This release includes:

- 🤖 YOLOv8, YOLOv11, YOLOv26 and RF-DETR object detection models trained on SeaDronesSee, spanning nano through x-large YOLO variants plus RF-DETR Nano/Small/Medium.
- 🌊 Benchmarked on SeaDronesSee's maritime search-and-rescue setting — swimmers, boats, jet skis, life-saving appliances and buoys captured by UAVs over open water, at varying altitudes and non-uniform image resolutions (1080p up to 4K+).
- 📊 Detailed model cards with mAP/precision/recall, per-class breakdowns, PR/F1 curves and confusion matrices (YOLO), qualitative detection showcases, and full training configurations for reproducibility.

Headline numbers:
- 🏆 Best mAP@50: 83.47% (RF-DETR Medium), 47.49% mAP@50:95, 87.01% precision.
- ⚡ Best efficiency tradeoff: YOLOv26s hits 80.14% mAP@50 at just 22.8 GFLOPs (10.0M params) — within ~3 points of the top RF-DETR variant, while actually beating YOLOv11x's 74.82% mAP@50 using ~8.6x fewer FLOPs (196.0 GFLOPs).

The goal is to make benchmarking and experimenting with maritime UAV perception easier by providing ready-to-use pretrained checkpoints, all trained and evaluated under one shared pipeline (DetectionBench: https://github.com/dronefreak/DetectionBench).

Full credit for the underlying dataset goes to Leon Amadeus Varga, Benjamin Kiefer, Martin Messmer, and Andreas Zell (University of Tübingen, WACV 2022) — this release is an unofficial, YOLO-ready reformatting of their work (CC0-licensed), not a new dataset.

If you're working on maritime search-and-rescue, UAV perception, autonomous drones, or real-time object detection, I hope these resources are useful.

📦 Dataset:
dronefreak/SeaDronesSee

🤖 Model Collection: dronefreak/seadronessee-object-detection-model-zoo-6a7b030a25797e5dd2d70123

Feedback, bug reports, and contributions are always welcome.

2 replies

dronefreak

posted an update about 2 months ago

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🚀 Excited to open-source the GWHD Wheat Head Detection Model Zoo on Hugging Face.

This release includes:

- 🤖 YOLOv8, YOLOv11, YOLOv26 and RF-DETR object detection models trained on GWHD (Global Wheat Head Dataset), spanning nano through x-large variants across both architecture families.
- 🌾 Benchmarked on GWHD's dense, single-class wheat-head detection task — ~45 annotated heads per image on average, captured across multiple countries, genotypes, and growth stages, a genuinely hard small/dense-object setting.
- 📊 Detailed model cards with mAP/precision/recall, per-class breakdowns, PR/F1 curves and confusion matrices (YOLO), qualitative detection showcases, and full training configurations for reproducibility.

Headline numbers:
- 🏆 Best mAP@50: 74.25% (YOLOv11x), 34.92% mAP@50:95, 83.37% precision.
- ⚡ Best efficiency tradeoff: YOLOv26s hits 70.49% mAP@50 at just 22.8 GFLOPs (10.0M params) — within ~4 points of the top YOewer FLOPs (196.0 GFLOPs).

The goal is to make benchmarking and experimenting with agricultural computer vision easier by providing ready-to-use pretrained checkpoints, all trained and evaluated under one shared pipeline (DetectionBench: https://github.com/dronefreak/DetectionBench).
Full credit for the underlying dataset goes to Etienne David, Mario Serouart, Simon Madec, and the Global Wheat Head Detection 2020/2021) — this release is anunofficial, YOLO-ready reformatting of their work, not a new dataset.

If you're working on precision at detection, or just want areproducible detector benchmark, I hope these resources are useful.

📦 Dataset:
dronefreak/GWHD

🤖 Model Collection: https://huggingface.co/collections/dronefreak/gwhd-wheat-head-detection-model-zoo-6a7aea28b5431918cc46cec1

Feedback, bug reports, and contributions are always welcome.

3 replies

Nymbo

posted an update about 2 months ago

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Anthropic gave me six months of Claude Max 20x through the Claude for Open Source program, granted based on my Hugging Face work. Thank you

Anthropic for supporting open source.

So far I've been pointing it at Markdown Minimap, an Obsidian plugin that adds a scrollable IDE-style minimap to your notes. This week I've been clearing a backlog of user-reported issues on it, with Claude often handling them end to end.

https://github.com/Nymbo/Markdown-Minimap — issues and PRs welcome.

dronefreak

posted an update 2 months ago

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🚀 Excited to open-source the VDD Semantic Segmentation Model Zoo on Hugging Face.

This release includes:

- 🤖 CABiNet and YOLO26 semantic segmentation models trained on VDD (Varied Drone Dataset), spanning Nano through XLarge YOLO26 variants plus a CABiNet (MobileNetV3-Large) baseline.
- 🌍 Benchmarked on VDD's varied altitudes, viewpoints, and scenes (urban, rural, natural) — a more diverse and challenging setting than single-flight UAV footage.
- 📊 Detailed model cards with evaluation metrics, per-class IoU, confusion matrices, qualitative RGB / Ground-Truth / Prediction comparisons, and training configurations for reproducibility.

Headline numbers:
- 🏆 Best mIoU: 78.83% (YOLO26x-sem)
- ⚡ Best efficiency tradeoff: CABiNet-Large hits 77.76% mIoU at just 54.8 GFLOPs — within 1-2 points of the top YOLO26 variantO26x's 430.9 GFLOPs)

The goal is to make benchmarking and experimenting with aerial semantic segmentation easier by providing ready-to-use pretraineat, all trained and evaluatedunder one shared pipeline.

If you're working on UAV perception, autonomous drones, robotics, remote sensing, or real-time semantic segmentation, I hope these resources are useful.

📦 Dataset: RussRobin/VDD

🤖 Model Collection: https://huggingface.co/collections/dronefreak/vdd-semantic-segmentation-model-zoo

Feedback, bug reports, and contributions are always welcome.

Nymbo

posted an update 2 months ago

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Introducing Inflect-v2, two exceptionally small, open-weight English TTS models at just 3.9M and 9.3M parameters. Both generate speech multiple times faster than real-time on CPU. Despite their size, Inflect-v2 delivers quality that is competitive with much larger lightweight TTS systems, including KittenTTS, Piper, and Supertonic-3.

CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.

See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2

Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)

6 replies

dronefreak

authored 4 papers 2 months ago

A Real-time Control Approach for Unmanned Aerial Vehicles using Brain-computer Interface

Paper • 1809.00346 • Published Sep 2, 2018 • 2

Learning to Navigate Autonomously in Outdoor Environments : MAVNet

Paper • 1809.00396 • Published Sep 2, 2018 • 1

A Supervised Learning Methodology for Real-Time Disguised Face Recognition in the Wild

Paper • 1809.02875 • Published Sep 8, 2018 • 1

Real-time Semantic Segmentation with Context Aggregation Network

Paper • 2011.00993 • Published Apr 11, 2021 • 6

dronefreak

posted an update 3 months ago

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🚀 Excited to open-source the **UAVid Semantic Segmentation Model Zoo** on Hugging Face.

This release includes:

* 📦 A **YOLO-compatible mirror** of the UAVid semantic segmentation dataset, preserving the original train/val/test splits while reorganizing the directory structure for plug-and-play use with modern training pipelines.
* 🤖 Multiple **YOLO26 semantic segmentation models** trained on UAVid, spanning Nano through Medium variants.
* 📊 Detailed model cards with evaluation metrics, per-class IoU, confusion matrices, qualitative results, and training configurations for reproducibility.

The goal is to make benchmarking and experimenting with aerial semantic segmentation easier by providing ready-to-use datasets and pretrained models in a consistent format.

If you're working on UAV perception, autonomous drones, robotics, remote sensing, or real-time semantic segmentation, I hope these resources are useful.

**📦 Dataset:** dronefreak/UAVid-2020

**🤖 Model Collection:** https://huggingface.co/collections/dronefreak/uavid-semantic-segmentation-model-zoo

Feedback, bug reports, and contributions are always welcome.

albertvillanova

posted an update 3 months ago

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🎉 KTO is now part of the stable TRL API

As of Promote KTO to stable API, KTOTrainer and KTOConfig have graduated from trl.experimental to the stable trl API. https://github.com/huggingface/trl/pull/6175

This one closes out a long road. Over the past 6+ months, the "Align KTO with DPO" effort landed ~90 PRs methodically bringing KTO up to the standard we hold for stable trainers, one carefully-scoped change at a time:
- Feature parity with DPO: full VLM support (incl. multi-image), sync_ref_model, PEFT + Liger, ZeRO-3 + PEFT dtype fix, pad_to_multiple_of, activation offloading, IterableDataset and dict eval_dataset, remove_unused_columns, and reference-logprob precomputation at init.
- Consistency with DPO: aligned method order and signatures, tokenization, _prepare_dataset, PEFT handling, ref-model preparation for distributed training, and config layout — plus a new DataCollatorForKTO and output format. Metrics moved into _compute_loss and simplified to direct averages via the shared _metrics attribute.
- Removing legacy baggage: dropped encoder-decoder support, BOS/EOS handling, null_ref_context, generate_during_eval, model_init, preprocess_logits_for_metrics, model/ref adapter names, and several dead config knobs.
- Coverage: a full test suite mirroring DPO, text collator tests, VLM tests, and slow tests.
- The promotion itself: the experimental → stable move (#6175) and shim cleanup (#6287), handled so downstream users get a clean deprecation path.

Honestly, this has been one of the more complex tasks I've taken on since joining the team, not because any single change was hard, but because it demanded sustained consistency across a ~2,000-line trainer, with every branch, comment, and edge case kept in lockstep with DPO.

Huge thanks to everyone who reviewed along the way (especially @qgallouedec ), the incremental review cadence is exactly what kept this maintainable.

KTO now sits on equal footing with our other flagship trainers. 🚀

2 replies

Shrijanagain

posted an update 3 months ago

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Welcome Researcher and Developers!

SKT AI Labs, we are pushing the boundaries of AI architecture and research—and today, we are thrilled to open our doors to the global research community!

We warmly welcome researchers, developers, and AI enthusiasts to join us and contribute to our R&D efforts.

🧪 What You Can Explore:

We invite you to experiment with our WMF (Weight Manifold Fusion) technology. You can test this high-dimensional fusion technique on smaller models to gain a deeper understanding of its behavior and token convergence.

---------- CHECK OUT:

SPACE : SKT-NRS/RD
EXPERIMENT : https://huggingface.co/sKT-Ai-Labs/SKT-SURYA-H
DIRECT TO MAIN DISCUSSION : SKT-NRS/RD#1

🤝 Your Feedback Shapes the Future :

If it works: Fantastic! Share your results with us and contribute directly to the core vision of SKT AI Labs.

If it doesn't work: No problem at all! Your critical feedback is just as valuable to us. Every experiment and anomaly helps us refine this architecture to make it more stable and robust.

We firmly believe that true innovation stems from community collaboration and transparent testing. Let's build the future of advanced AI together. Your ideas, test results, and feedback are always welcome!

You Can Still Research and Development On WMF Only SKT-SURYA-H Model is Dismissed.

Let's innovate and build together! 💡

Shrijanagain

posted an update 3 months ago

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311

🚀 Big News for the AI Community! 🔥

We’re excited to release NRS_QWEN_MYTHOS_1M — a powerful reasoning model built on Qwen 3.5 9B!
At SKT AI LABS, we’ve supercharged this 9B model with our proprietary Neural Reasoning System (NRS) to deliver next-level performance.

🔥 Why This Model is a Game-Changer:
✅ 100x Reasoning Capacity — Exceptional deep logical thinking and complex problem-solving
✅ 1 Million Token Context — Perfect for massive codebases, long documents, and multi-turn agentic workflows
✅ Advanced Thinking Mode — Native <think> tags for true step-by-step Chain-of-Thought reasoning
✅ Tool-Use Ready — Optimized for Python execution, Web Search, and self-correction
✅ Blazing Fast — Runs smoothly on consumer GPUs like RTX 3090/4090

Technical Highlights:

Base: Qwen 3.5 9B
Tuning: NRS-specific high-quality reasoning data
Context: 1M Tokens (YaRN Scaling)
License: NRS DOCS

Whether you’re a developer building coding agents, a researcher working with long-context data, or someone who loves powerful reasoning — this model is built for you.

👉 Try it now on Hugging Face:
SKT-NRS/NRS_QWEN_MYTHOS_1M

Drop a comment: What will you build with it first? 👇
#AI #OpenSource #LLM #Qwen #ReasoningModel #HuggingFace #NewModel #AICommunity

j-chim

authored a paper 3 months ago

The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset

Paper • 2303.03915 • Published Mar 7, 2023 • 9

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