Pith. sign in

REVIEW 4 cited by

Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2211.13523 v3 pith:QYD77T63 submitted 2022-11-24 cs.CV

Floriana Ciaglia , Francesco Saverio Zuppichini , Paul Guerrie , Mark McQuade , Jacob Solawetz This is my paper · ORCID
classification cs.CV
keywords datasetsrf100benchmarkimagesdetectiondomainsmodelmulti-domain
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The evaluation of object detection models is usually performed by optimizing a single metric, e.g. mAP, on a fixed set of datasets, e.g. Microsoft COCO and Pascal VOC. Due to image retrieval and annotation costs, these datasets consist largely of images found on the web and do not represent many real-life domains that are being modelled in practice, e.g. satellite, microscopic and gaming, making it difficult to assert the degree of generalization learned by the model. We introduce the Roboflow-100 (RF100) consisting of 100 datasets, 7 imagery domains, 224,714 images, and 805 class labels with over 11,170 labelling hours. We derived RF100 from over 90,000 public datasets, 60 million public images that are actively being assembled and labelled by computer vision practitioners in the open on the web application Roboflow Universe. By releasing RF100, we aim to provide a semantically diverse, multi-domain benchmark of datasets to help researchers test their model's generalizability with real-life data. RF100 download and benchmark replication are available on GitHub.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DETR-ViP: Detection Transformer with Robust Discriminative Visual Prompts

    cs.CV 2026-04 unverdicted novelty 7.0 of 10

    Global prompt integration, visual-textual relation distillation and selective fusion make visual prompts discriminative enough for DETR-ViP to beat prior visual-prompt detectors by several mAP points.

  2. Pseudo-Labeling Driven Refinement of Benchmark Object Detection Datasets via Analysis of Learning Patterns

    cs.CV 2025-06 reject novelty 6.0 of 10

    MJ-COCO, a pseudo-labeling based re-annotation of MS-COCO, improves detection on some external benchmarks but reduces performance on the standard MS-COCO validation set.

  3. FLORA: Efficient Synthetic Data Generation for Object Detection in Low-Data Regimes via finetuning Flux LoRA

    cs.CV 2025-08 conditional novelty 5.0 of 10

    A LoRA fine-tuned Flux inpainting pipeline generates synthetic object detection images that outperform ODGEN's 10x larger synthetic set in downstream mAP.

  4. Deep learning framework for crater detection and identification on the Moon and Mars

    cs.CV 2025-08 conditional novelty 3.0 of 10

    On Moon and Mars imagery, YOLO yields the most balanced crater detection, while ResNet-50 excels at large craters, under a two-stage CNN-based framework.

Pith tools