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Papers/On Generalizing Detection Models for Unconstrained Environ...

On Generalizing Detection Models for Unconstrained Environments

Prajjwal Bhargava

2019-09-28Transfer LearningIncremental Learningobject-detectionObject Detection
PaperPDFCode(official)

Abstract

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.

Results

TaskDatasetMetricValueModel
Object DetectionBDD100K valmAP@0.545.7hybrid incremental net
Object DetectionIndia Driving DatasetmAP@0.531.57hybrid incremental net
3DBDD100K valmAP@0.545.7hybrid incremental net
3DIndia Driving DatasetmAP@0.531.57hybrid incremental net
2D ClassificationBDD100K valmAP@0.545.7hybrid incremental net
2D ClassificationIndia Driving DatasetmAP@0.531.57hybrid incremental net
2D Object DetectionBDD100K valmAP@0.545.7hybrid incremental net
2D Object DetectionIndia Driving DatasetmAP@0.531.57hybrid incremental net
16kBDD100K valmAP@0.545.7hybrid incremental net
16kIndia Driving DatasetmAP@0.531.57hybrid incremental net

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