Self-Supervised YOLO: Leveraging Contrastive Learning for Label-Efficient Object Detection

Manikanta Kotthapalli; Reshma Bhatia; Nainsi Jain

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arXiv · 2508.01966

Self-Supervised YOLO: Leveraging Contrastive Learning for Label-Efficient Object Detection

Manikanta Kotthapalli·Reshma Bhatia·Nainsi Jain

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Abstract

One-stage object detectors such as the YOLO family achieve state-of-the-art performance in real-time vision applications but remain heavily reliant on large-scale labeled datasets for training. In this work, we present a systematic study of contrastive self-supervised learning (SSL) as a means to reduce this dependency by pretraining YOLOv5 and YOLOv8 backbones on unlabeled images using the SimCLR framework. Our approach introduces a simple yet effective pipeline that adapts YOLO's convolutional backbones as encoders, employs global pooling and projection heads, and optimizes a contrastive loss using augmentations of the COCO unlabeled dataset (120k images). The pretrained backbones are then fine-tuned on a cyclist detection task with limited labeled data. Experimental results show that SSL pretraining leads to consistently higher mAP, faster convergence, and improved precision-recall performance, especially in low-label regimes. For example, our SimCLR-pretrained YOLOv8 achieves a mAP@50:95 of 0.7663, outperforming its supervised counterpart despite using no annotations during pretraining. These findings establish a strong baseline for applying contrastive SSL to one-stage detectors and highlight the potential of unlabeled data as a scalable resource for label-efficient object detection.

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Manikanta Kotthapalli, Reshma Bhatia, Nainsi Jain. 2025-08-04. Self-Supervised YOLO: Leveraging Contrastive Learning for Label-Efficient Object Detection. https://arxiv.org/abs/2508.01966

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