Fine-grained visual categorization (FGVC) is a challenging but significant task in computer vision, which aims to recognize different sub-categories of birds, cars, airplanes, etc. Among them, recognizing models of different cars has significant application value in autonomous driving, traffic surveillance and scene understanding, which has received considerable attention in the past few years. However, Stanford-Car, the most widely used fine-grained dataset for car recognition, only has 196 different categories and only includes vehicle models produced earlier than 2013. Due to the rapid advancements in the automotive industry during recent years, the appearances of various car models have become increasingly intricate and sophisticated. Consequently, the previous Stanford-Car dataset fails to capture this evolving landscape and cannot satisfy the requirements of automotive industry. To address these challenges, in our paper, we introduce Car-1000, a large-scale dataset designed specifically for fine-grained visual categorization of diverse car models. Car-1000 encompasses vehicles from 166 different automakers, spanning a wide range of 1000 distinct car models. Additionally, we have reproduced several state-of-the-art FGVC methods on the Car-1000 dataset, establishing a new benchmark for research in this field. We hope that our work will offer a fresh perspective for future FGVC researchers. Our dataset is available at https://github.com/toggle1995/Car-1000.

翻译：细粒度视觉分类（FGVC）是计算机视觉领域一项具有挑战性且意义重大的任务，其目标在于识别鸟类、汽车、飞机等不同对象的精细子类别。其中，对不同车型的识别在自动驾驶、交通监控和场景理解中具有重要的应用价值，在过去几年中受到了广泛关注。然而，目前最广泛使用的汽车细粒度识别数据集Stanford-Car仅包含196个不同类别，且仅收录了2013年之前生产的车型。由于近年来汽车工业的快速发展，各类车型的外观设计日趋复杂与精细。因此，原有的Stanford-Car数据集已无法反映这一发展现状，难以满足汽车行业的需求。为应对这些挑战，本文提出了Car-1000，这是一个专门为多样化车型的细粒度视觉分类而设计的大规模数据集。Car-1000涵盖了来自166家不同汽车制造商的车辆，包含多达1000种不同的车型。此外，我们在Car-1000数据集上复现了多种先进的FGVC方法，为该领域的研究建立了新的基准。我们希望这项工作能为未来的FGVC研究者提供新的视角。我们的数据集发布于 https://github.com/toggle1995/Car-1000。