# RAWDet-7: A Multi-Scenario Benchmark for Object Detection and Description on Quantized RAW Images

Source: [RAWDet-7: A Multi-Scenario Benchmark for Object Detection and Description on Quantized RAW Images](<https://www.sota2.com/research/paper/6982d2faa8b01e76cc8e99f3>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-12T05:00:04.496Z

Authors: Mishal Fatima, Shashank Agnihotri, Kanchana Vaishnavi Gandikota, Michael Moeller, Margret Keuper

Published: 2026-02-03T17:22:45.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2602.03760>)

[PDF](<https://arxiv.org/pdf/2602.03760.pdf>)