Papers › On Generalizing Detection Models for Unconstrained Environments
On Generalizing Detection Models for Unconstrained Environments
Prajjwal Bhargava
Object detection has seen tremendous progress in recent years. However, current algorithms don't generalize well when tested on diverse data distributions. We address the problem of incremental learning in object detection on the India Driving Dataset (IDD). Our approach involves using multiple domain-specific classifiers and effective transfer learning techniques focussed on avoiding catastrophic forgetting. We evaluate our approach on the IDD and BDD100K dataset. Results show the effectiveness of our domain adaptive approach in the case of domain shifts in environments.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Object Detection | BDD100K val | hybrid incremental net | mAP@0.5 | 45.7 | #1 of 1 | Archive leaderboard | report |
| Object Detection | India Driving Dataset | hybrid incremental net | mAP@0.5 | 31.57 | #2 of 4 | Archive leaderboard | report |
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