Thank you so much for your thoughtful reply and for the generous offer to collaborate on the DOTA Aerial Detection Benchmark. I am truly excited about the opportunity to contribute to this work, especially given my growing interest in object detection and YOLO-based models. I would be delighted to assist with the development side of things, and I am flexible with timing, weekends work perfectly for me as well. Please feel free to share any initial plans, datasets, or repositories you're working with, so I can start familiarizing myself with the codebase and the evaluation pipeline.
Md Masud Rana
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repliedto dronefreak's post about 5 hours ago
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. repliedto dronefreak's post 6 days ago
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. reacted to dronefreak's post with 🚀 6 days ago
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.Organizations
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replied to dronefreak's post about 5 hours ago
replied to dronefreak's post 6 days ago
Dear Mr. Saksena, I am a CSE student from Bangladesh, deeply inspired by your research in Computer Vision and Healthcare AI. I am currently working on deep learning projects and would be honored to connect and explore any future research collaboration opportunities with you. Respectfully, Md Masud Rana.
reacted to dronefreak's post with 🚀❤️🧠🔥 6 days ago
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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.
- 6 replies
reacted to SeaWolf-AI's post with 🔥 6 days ago
Post
6772
🚀 Introducing FINAL-Bench Quantum — an open, neutral benchmark that finally puts quantum-computing methods on one fair yardstick.
Quantum results are notoriously hard to compare. The same "logical error rate" or "query fidelity" means very different things depending on the code, noise model, hardware, and shot count. FINAL-Bench Quantum fixes that: five events judged under identical, published protocols, where every number is labeled as either measured here or quoted from a source.
Five events: ① QEC Decoder ② Optimization (Max-Cut) ③ VQE ④ QRAM ⑤ Quantum Simulation
The rules are simple and strict:
✅ Track A (measured here, with 95% confidence intervals) is kept separate from Track B (quoted from papers, not directly comparable).
🔬 Simulation and real hardware are clearly distinguished, and no quantum-advantage claims are made.
🌍 Methods from Google, IBM, NVIDIA, USTC, Riverlane and more sit side by side, with origin flags and author credits.
📤 Anyone can submit their own method via the Submit tab for review and listing.
Already on the board: real IBM Heron r2 measurements (repetition-code distance boundary, 29–175× error reduction from d3 to d5), a real-chip QRAM query fidelity of 0.92, and H₂ VQE at chemical accuracy — always labeled honestly as simulation vs hardware.
A leaderboard is only useful if you can trust it, so neutrality is the whole point: strong competitors stay in even when they beat the host, sources are quoted faithfully, and a simulation is never rounded up into a hardware claim.
Leaderboard: FINAL-Bench/quantum-bench-leaderboard
Article: https://huggingface.co/blog/FINAL-Bench/quantum-leaderboard
#quantum #QEC #QuantumComputing #benchmark
Quantum results are notoriously hard to compare. The same "logical error rate" or "query fidelity" means very different things depending on the code, noise model, hardware, and shot count. FINAL-Bench Quantum fixes that: five events judged under identical, published protocols, where every number is labeled as either measured here or quoted from a source.
Five events: ① QEC Decoder ② Optimization (Max-Cut) ③ VQE ④ QRAM ⑤ Quantum Simulation
The rules are simple and strict:
✅ Track A (measured here, with 95% confidence intervals) is kept separate from Track B (quoted from papers, not directly comparable).
🔬 Simulation and real hardware are clearly distinguished, and no quantum-advantage claims are made.
🌍 Methods from Google, IBM, NVIDIA, USTC, Riverlane and more sit side by side, with origin flags and author credits.
📤 Anyone can submit their own method via the Submit tab for review and listing.
Already on the board: real IBM Heron r2 measurements (repetition-code distance boundary, 29–175× error reduction from d3 to d5), a real-chip QRAM query fidelity of 0.92, and H₂ VQE at chemical accuracy — always labeled honestly as simulation vs hardware.
A leaderboard is only useful if you can trust it, so neutrality is the whole point: strong competitors stay in even when they beat the host, sources are quoted faithfully, and a simulation is never rounded up into a hardware claim.
Leaderboard: FINAL-Bench/quantum-bench-leaderboard
Article: https://huggingface.co/blog/FINAL-Bench/quantum-leaderboard
#quantum #QEC #QuantumComputing #benchmark
- 2 replies
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