Perception365/VehicleNet-Y26s
Object Detection • Updated • 1 • 9
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Subh775
updated a
model 6 days ago
dronefreak
posted an update 11 days ago
Post
217
🌧️❄️ 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).
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 12 days ago
Post
2128
🚀 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.
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 13 days ago
Post
1906
🚀 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: dronefreak/gwhd-wheat-head-detection-model-zoo-6a7aea28b5431918cc46cec1
Feedback, bug reports, and contributions are always welcome.
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: dronefreak/gwhd-wheat-head-detection-model-zoo-6a7aea28b5431918cc46cec1
Feedback, bug reports, and contributions are always welcome.
- 3 replies
Subh775
updated a
Space 15 days ago
dronefreak
posted an update 22 days ago
Post
930
🚀 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.
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 about 1 month ago
A Real-time Control Approach for Unmanned Aerial Vehicles using Brain-computer Interface
Paper • 1809.00346 • Published • 2
Learning to Navigate Autonomously in Outdoor Environments : MAVNet
Paper • 1809.00396 • Published • 1
A Supervised Learning Methodology for Real-Time Disguised Face Recognition in the Wild
Paper • 1809.02875 • Published • 1
Real-time Semantic Segmentation with Context Aggregation Network
Paper • 2011.00993 • Published • 6
dronefreak
posted an update about 1 month ago
Post
4557
🚀 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.
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 2 months ago
Post
3338
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.
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
Subh775
updated 6
models 4 months ago
Perception365/VehicleNet-RFDETR-n
Object Detection • Updated
Perception365/VehicleNet-RFDETR-m
Object Detection • Updated
Perception365/VehicleNet-RFDETR-s
Object Detection • 31.8M • Updated
Perception365/VehicleNet-Y26n
Object Detection • Updated • 2 • 1
Perception365/VehicleNet-Y26m
Object Detection • Updated • 3 • 1
Perception365/VehicleNet-Y26x
Object Detection • Updated • 10 • 1
Subh775
published a
Space 6 months ago