The Stanford Cars dataset (also referred to as Cars196) is a fine-grained image classification benchmark of car make/model/year. It contains 16,185 images of cars across ~196 classes (the original FGVC13 paper refers to 197 classes; common dataset distributions and usages report 196 classes). Images are labeled at the car model (often including year) and are commonly provided with a roughly 50/50 train/test split (8,144 training images and 8,041 test images). The dataset was collected and released by Jonathan Krause, Jia Deng, Michael Stark and Li Fei-Fei (Stanford); it is widely used for fine-grained categorization and metric-learning / retrieval experiments and often distributed with metadata (class labels, model/maker/year) and bounding-box annotations.
No results indexed yet — be the first to submit a score.
Submit a checkpoint and a reproduction script. We will run it, publish the score, and — if it takes the top — annotate the step on the progress chart with your name.