Most vision models are trained on RGB images processed through ISP pipelines optimized for human perception, which can discard sensor-level information useful for machine reasoning. RAW images preserve unprocessed scene data, enabling models to leverage richer cues for both object detection and object description, capturing fine-grained details, spatial relationships, and contextual information often lost in processed images. To support research in this domain, we introduce RAWDet-7, a large-scale dataset of ~25k training and 7.6k test RAW images collected across diverse cameras, lighting conditions, and environments, densely annotated for seven object categories following MS-COCO and LVIS conventions. In addition, we provide object-level descriptions derived from the corresponding high-resolution sRGB images, facilitating the study of object-level information preservation under RAW image processing and low-bit quantization. The dataset allows evaluation under simulated 4-bit, 6-bit, and 8-bit quantization, reflecting realistic sensor constraints, and provides a benchmark for studying detection performance, description quality & detail, and generalization in low-bit RAW image processing. Dataset & code upon acceptance.

翻译：大多数视觉模型在通过为人类感知优化的ISP（图像信号处理）流水线处理后的RGB图像上进行训练，这可能会丢弃对机器推理有用的传感器级信息。RAW图像保留了未经处理的场景数据，使模型能够利用更丰富的线索进行目标检测和目标描述，捕捉通常在处理图像中丢失的细粒度细节、空间关系和上下文信息。为支持该领域的研究，我们引入了RAWDet-7，这是一个大规模数据集，包含约25,000张训练图像和7,600张测试图像，这些RAW图像采集自不同的相机、光照条件和环境，并按照MS-COCO和LVIS的标注规范对七个目标类别进行了密集标注。此外，我们提供了从对应高分辨率sRGB图像中提取的目标级描述，便于研究RAW图像处理和低位量化下的目标级信息保留。该数据集支持在模拟的4位、6位和8位量化条件下进行评估，以反映真实的传感器约束，并为研究低位RAW图像处理中的检测性能、描述质量与细节以及泛化能力提供了一个基准。数据集与代码将在论文录用后发布。