EviLens

Images for EviLens, a human-verified benchmark of 688 instances for evaluating perception under insufficient evidence — cases where a single glance at the image is not enough to produce the required perceptual output.

Conventional perception benchmarks assume the image plus the model's parametric knowledge suffice to resolve the query. EviLens deliberately breaks that assumption in three ways: the target may be too small to resolve without inspecting the image at a finer scale (in many cases well under 0.1% of the image area), it may only be findable by comparing or scanning multiple regions, or identifying it may require knowledge from outside the image. The model must still commit to a box, a mask, or a count.

Composition

Category	n	Missing evidence	Metrics
Localization	140	Target is explicitly named but tiny or hidden among clutter (high-resolution scenes, I-spy puzzles)	mean box IoU, R@0.5
Recognition	182	Target is visually salient but referred to indirectly; needs external information to identify	mean box IoU, R@0.5
Spot-the-difference	15	Target is defined relative to a second panel; needs cross-panel comparison	micro-F1, macro-F1
Segmentation	195	Recognition setting, but the output is a mask	gIoU, cIoU
Counting	156	Recognition setting, but the output is an integer	exact-match accuracy

Localization, recognition, and spot-the-difference are the three grounding categories. The 15 spot-the-difference instances contain 79 annotated differences in total. Every instance is manually verified.

Metric definitions

• IoU / R@0.5 — mean box IoU, and the fraction of instances with IoU ≥ 0.5.
• gIoU / cIoU — mean per-instance mask IoU, and cumulative intersection over cumulative union. Predicted boxes are converted to masks with SAM3.
• micro-F1 / macro-F1 (spot-the-difference) — greedy one-to-one matching at IoU 0.5 between predicted boxes and annotated differences. Micro-F1 pools counts across images; macro-F1 averages per-image F1. Micro-F1 is the primary metric: with 15 images and 79 differences, it is the more stable of the two.
• Coordinates are canonically xyxy, normalized to [0, 1000].

Getting started

The evaluation harness fetches these images for you:

git clone <code repository>
cd EviLens
pip install -r requirements.txt

# downloads this dataset into benchmark/images/ and verifies every file
# against scripts/images_manifest.json (size + sha256)
python3 scripts/download_images.py

# evaluate any OpenAI-compatible endpoint
MODEL_NAME=my-model BASE_URL=https://api.example.com/v1 API_KEY=... \
  ./run_eval.sh my_run

To fetch the images on their own:

from huggingface_hub import snapshot_download
snapshot_download("bunny127/EviLens", repo_type="dataset",
                  local_dir="benchmark", allow_patterns=["images/**"])

Layout

images/
├── counting_rl/images/...                 156 files   240 MB
├── detailed_rl/{images,images_hard,...}   151 files   359 MB
└── searched/images/...                    367 files   280 MB

Each benchmark record carries image_tag and image_path; the image resolves to images/<image_tag>/<image_path>. 674 unique files serve the 688 instances — spot-the-difference pairs and a few cross-category instances share an image.

Data Sources, Licensing, and Responsible Use

EviLens is released for non-commercial academic research on evidence-seeking visual perception.

The identity-related examples focus on publicly documented figures and fictional characters. Real-person photographs were collected from publicly accessible web sources, with identity information derived from publicly available information. Some examples use synthetically generated group photographs depicting public figures in fictional scenes. These synthetic images are intended as constructed research examples and should not be interpreted as records of actual events.

The benchmark annotations and other original materials created by the authors are released under CC BY-NC 4.0. Third-party images remain subject to the rights and applicable terms of their respective copyright holders. Their inclusion in this research dataset does not constitute a separate license to those images.

The dataset is intended for evaluating multimodal perception and evidence-seeking, not for identifying private individuals or conducting surveillance. Users are responsible for complying with applicable licensing, privacy, and data-protection requirements.

Rights holders or individuals with concerns about specific images may contact the maintainers through the repository's Community discussions to request review and appropriate removal.

Citation

EviLens is introduced in EviRover: Reinforcing Agentic Perception Beyond a Glance.

@article{fan2026evirover,
  title={EviRover: Reinforcing Agentic Perception Beyond a Glance},
  author={Fan, Kaixuan and Feng, Kaituo and Peng, Tianshuo and Jiang, Yilei and Zhang, Manyuan and Wang, Junke and Yue, Xiangyu},
  journal={arXiv preprint arXiv:2609.40230},
  year={2026}
}