humansensinglab/AGenDA
[ICCV 2025] Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision
This project helps refine vehicle detection models used with drone or satellite imagery, particularly when the conditions or locations in new images are different from those the model was originally trained on. It takes existing aerial images (some with weak labels) and an initial vehicle detection model, then outputs an improved model better suited for diverse, new environments. Analysts in aerial surveillance or urban planning who rely on automated vehicle counts will find this useful.
Use this if you need to reliably count or detect vehicles in aerial imagery from a wide range of geographical locations or weather conditions, even if your existing models struggle with these new scenarios.
Not ideal if you are looking for a pre-trained, ready-to-use vehicle detection model without needing to adapt it to new or challenging aerial imaging environments.
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Language
Python
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Last pushed
Dec 01, 2025
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