Transactions on Machine Learning Research (TMLR) · 2026

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

M. Fatima, S. Agnihotri, K. V. Gandikota, M. Moeller, Margret Keuper

Why this publication matters

Camera processing is usually designed to make pictures pleasant for people, but machines may need different information. RAWDet-7 tests recognition and description directly on sensor data with different levels of numerical precision. It provides evidence for studying the trade-off between data efficiency and useful visual information.

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 finegrained 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.

Abstract source ↗

Figures

RAW image scenes with object detections and region-level descriptions. — **Figure 1.** Comparing ground truth annotations provided in the original datasets and the new ones proposed in RAWDET-7. Our proposed annotations are more fine-grained, as seen for PASCAL RAW, which originally annotated only one instance of the cars in the image; we now annotate all the other instances of cars in the image (with a 20% overlap threshold i.e. at least 20% area of the bounding box should be non-overlapping). RAW NOD (-Nikon and -Sony), RAOD (-Day and -Night) original annotations contain hallucinations as seen here for RAW NOD, which hallucinates a bicycle in the center right of the frame. Original annotations even contain some misclassifications, as seen for RAOD, which misclassified a motorcycle as a person. RAWDET-7 (bottom) overcomes these drawbacks. View in source ↗

**Figure 2.** Benchmarking performance on RAWDET-7. Baselines such as logarithmic quantization and jointly learnt γ improve results across quantization levels and architectures. View in source ↗

Cite this paper

Download .bib

@article{fatima2026rawdet7a90,
  title = {{RAWDet-7 A Multi-Scenario Benchmark for Object Detection and Description on Quantized RAW Images}},
  author = {M. Fatima and S. Agnihotri and K. V. Gandikota and M. Moeller and Margret Keuper},
  journal = {Transactions on Machine Learning Research},
  year = {2026},
  url = {https://openreview.net/forum?id=UHTJrsYieo}
}

Figures and abstract are reproduced from the linked research sources. Credit remains with the authors and publishers.