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Mitigating Conversational Inertia in Multi-Turn Agents

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UI-Venus-2 Technical Report

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lora-library/inme:Add application file

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dronefreak

posted an update 4 days ago

Post

121

🚀 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 8 days ago

Post

124

🚀 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 10 days ago

Post

117

🚀 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 11 days ago

Post

🚀 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

Post

437

🌧️❄️ 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

dronefreak

posted an update about 2 months ago

Post

2139

🚀 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

Post

1916

🚀 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

dronefreak

posted an update 2 months ago

Post

934

🚀 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.

dronefreak

authored 4 papers 3 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

Post

4561

🚀 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.

dronefreak

posted an update 4 months ago

Post

3341

Excited to open-source the VisDrone Aerial Object Detection Model Zoo on Hugging Face.

The collection includes multiple YOLO variants trained and evaluated on the VisDrone benchmark for aerial object detection, with accompanying documentation and performance metrics.

If you're working on drones, aerial surveillance, robotics, or small-object detection, I hope these models save you some time.

Model Zoo: https://huggingface.co/collections/dronefreak/visdrone-detection-model-zoo

Feedback, issues, and contributions are welcome.

13 replies

stephenb1334

in lora-library/inme 4 months ago

Add application file

#1 opened 4 months ago by

stephenb1334

blanchon

posted an update 5 months ago

Post

3123

I'm releasing OpenCS2 a 11TB dataset of around 5000 hours of counter strike gameplay recording.
- HD resolution - 1280×720 · 32 fps
- For each frame keyboard and mouse + world state (player position, velocity, weapon ...)
- HD Stereo audio
- All 10 players perspective

https://huggingface.co/collections/blanchon/opencs2