TL;DR
This paper presents a highly efficient object detection method capable of processing over 200 frames per second, utilizing a lightweight architecture, distillation techniques, and unlabeled data to enhance speed and accuracy.
Contribution
The authors introduce a novel lightweight network architecture combined with an innovative distillation loss and unlabeled data training, achieving high speed and improved accuracy.
Findings
Achieves over 200 FPS in object detection.
Network has 10x fewer parameters than VGG-based models.
Improves detection accuracy by 14 mAP on Pascal dataset.
Abstract
In this paper, we propose an efficient and fast object detector which can process hundreds of frames per second. To achieve this goal we investigate three main aspects of the object detection framework: network architecture, loss function and training data (labeled and unlabeled). In order to obtain compact network architecture, we introduce various improvements, based on recent work, to develop an architecture which is computationally light-weight and achieves a reasonable performance. To further improve the performance, while keeping the complexity same, we utilize distillation loss function. Using distillation loss we transfer the knowledge of a more accurate teacher network to proposed light-weight student network. We propose various innovations to make distillation efficient for the proposed one stage detector pipeline: objectness scaled distillation loss, feature map non-maximal…
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Taxonomy
MethodsDropout · Dense Connections · *Communicated@Fast*How Do I Communicate to Expedia? · Max Pooling · Softmax · Convolution · Ethereum Customer Service Number +1-833-534-1729
