Tag: Object-Detection
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SSDD in oriented-det — optical DOTA to SAR ships in twelve epochs
Native SSDD SAR ship loader in oriented-det v0.3: keep-ratio 608, finetune DOTA Faster R-CNN 1×, held-out last-digit test 90.34% mAP50. No SSDD Hub zoo.
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FAIR1M in oriented-det — 37 classes, and why 36.70% is not a failed train
Native FAIR1M support in oriented-det v0.3: convert + tile, finetune DOTA 1× Faster R-CNN, tiled-val 36.70% mAP50. The bottleneck is class ID, not boxes. No Hub zoo — CC BY-NC-SA dump.
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HRSC2016 in oriented-det — recipes, trains, and a held-out 90.41%
Native HRSC2016 ship detection in oriented-det: ImageSets trainval in, held-out test out, three 3× Hub weights. Oriented R-CNN 90.41%, Faster R-CNN 88.77%, FCOS 88.34% mAP50 on NVIDIA L4.
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A static demo of three oriented detectors on optical satellite imagery
A browser demo of Oriented-Det’s three 3× DOTA checkpoints — Oriented R-CNN, Rotated Faster R-CNN, and Rotated FCOS — on six optical satellite scenes, with a side by side comparison that lands in parity with state of the art frameworks.
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Detecting ships in satellite imagery: five years later…
Revisiting ship detection in SPOT imagery — from Kaggle to MMRotate.
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Is YOLOv8 suitable for satellite imagery?
Tuning YOLOv8 for satellite imagery — what works and what to change.
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How to choose a deep learning architecture to detect aircrafts in satellite imagery?
Comparing deep learning architectures for aircraft detection in satellite imagery.
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Oil Storage Detection on Airbus Imagery with YOLOX
Detecting oil storage tanks on Airbus imagery with YOLOX.
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Detecting aircraft on Airbus Pleiades imagery with YOLOv5
Training YOLOv5 on Airbus Pleiades imagery for aircraft detection.