CaMiT: A Time-Aware Car Model Dataset for Classification and Generation

• URL: http://arxiv.org/abs/2510.17626v2
• Date: Tue, 21 Oct 2025 13:49:24 GMT
• Title: CaMiT: A Time-Aware Car Model Dataset for Classification and Generation
• Authors: Frédéric LIN, Biruk Abere Ambaw, Adrian Popescu, Hejer Ammar, Romaric Audigier, Hervé Le Borgne,
• Abstract summary: We introduce Car Models in Time (CaMiT), a fine-grained dataset capturing the temporal evolution of car models.<n>CaMiT includes 787K labeled samples of 190 car models (2007-2023) and 5.1M unlabeled samples (2005-2023), supporting both supervised and self-supervised learning.
• Score: 7.326527258062973
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: AI systems must adapt to evolving visual environments, especially in domains where object appearances change over time. We introduce Car Models in Time (CaMiT), a fine-grained dataset capturing the temporal evolution of car models, a representative class of technological artifacts. CaMiT includes 787K labeled samples of 190 car models (2007-2023) and 5.1M unlabeled samples (2005-2023), supporting both supervised and self-supervised learning. Static pretraining on in-domain data achieves competitive performance with large-scale generalist models while being more resource-efficient, yet accuracy declines when models are tested across years. To address this, we propose a time-incremental classification setting, a realistic continual learning scenario with emerging, evolving, and disappearing classes. We evaluate two strategies: time-incremental pretraining, which updates the backbone, and time-incremental classifier learning, which updates only the final layer, both improving temporal robustness. Finally, we explore time-aware image generation that leverages temporal metadata during training, yielding more realistic outputs. CaMiT offers a rich benchmark for studying temporal adaptation in fine-grained visual recognition and generation.

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