Yusuf Chowdury
Yusufchy
AI & ML interests
AI agents, open-weight models, machine learning, MLOps, developer tools, AI automation, and AI-assisted publishing.
Recent Activity
repliedto dronefreak's post 7 minutes ago
🚀 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: https://huggingface.co/collections/dronefreak/seadronessee-object-detection-model-zoo-6a7b030a25797e5dd2d70123
Feedback, bug reports, and contributions are always welcome. repliedto SoulInPsyAbstract's post about 3 hours ago
Meta released Muse Glimmer 30B on Aug 10. We fine-tuned it the next day.
Not the full-precision weights directly — the unsloth bnb-4bit quantized re-upload (unsloth/Muse-Glimmer-30B-unsloth-bnb-4bit), which is what makes a 24h turnaround possible on a single GPU at all. Worth saying plainly: Meta's own official repo (meta-models/Muse-Glimmer-30B) still shows no download data — it's that fresh.
What we tuned it on: not new facts, a pattern. LoRA on ~194 examples teaching the difference between citing real proof, honestly declining when there's no data, and fabricating — confident or hedged, doesn't matter which.
Results on 20 held-out claims never seen in training:
- base model: 0/20
- tuned: 20/20
Training: 472.5s, loss 0.799 → 0.086.
Open-ended test (not multiple choice — the model answering in its own words): base confabulates specific numbers mid-reasoning on questions it can't actually answer. Tuned: declines cleanly, every time.
Dataset: https://huggingface.co/datasets/SoulInPsyAbstract/specialist-cd-binary-honesty
Adapter: https://huggingface.co/SoulInPsyAbstract/specialist-cd-muse-glimmer-lora
Meta's release: https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model
Same non-fabrication pattern also holds on Hermes-3-8B and Qwen2.5-7B, tested with the identical held-out set. Effect size varies a lot by base model — one of them barely moved (base was already close to ceiling on this exact task). More on that soon.Organizations
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