🤝 Open to Collab

Saumya Saksena

dronefreak

147 6 4

https://saumyasaksena.com

AI & ML interests

Computer Vision, Deep Learning, Image Restoration, Image De-noising, LLMs, RAG, Image Classification, Image Segmentation, EEG Classification, Signal Processing, PPG, BCI

Recent Activity

posted an update about 14 hours ago

🚀 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: https://huggingface.co/collections/dronefreak/kitti-object-detection-model-zoo-6ab4cb94fb08dccf3bde2e8b Feedback and contributions welcome.

updated a collection 2 days ago

KITTI Object Detection Model Zoo

updated a model 2 days ago

dronefreak/kitti-yolov9t

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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.