CraterBench-R

CraterBench-R is an instance-level crater retrieval benchmark built from Mars CTX imagery.

This repository contains the released benchmark images, official split files, relevance metadata, and a minimal evaluation example.

Summary

• 25,000 crater identities in the benchmark gallery
• 50,000 gallery images with two canonical context crops per crater
• 1,000 held-out query crater identities
• 5,000 manually verified query images with five views per query crater
• official train and test split manifests with relative paths only

Download

# Download the repository
pip install huggingface_hub
huggingface-cli download jfang/CraterBench-R --repo-type dataset --local-dir CraterBench-R
cd CraterBench-R

# Extract images
unzip images.zip

After extraction the directory should contain images/gallery/ (50,000 JPEGs) and images/query/ (5,000 JPEGs).

Repository Layout

• images.zip: all benchmark images (gallery + query) in a single archive
• splits/test.json: official benchmark split with full gallery plus query set
• splits/train.json: train gallery with the full test relevance closure removed
• metadata/query_relevance.json: raw co-visible crater IDs and gallery-filtered relevance
• metadata/stats.json: release summary statistics
• metadata/source/retrieval_ground_truth_raw.csv: raw query relevance CSV for reference
• examples/eval_timm_global.py: minimal global-descriptor example for any timm image model
• requirements.txt: lightweight requirements for the example script

Split Semantics

splits/test.json is the official benchmark split and includes both:

• the full gallery
• the query set evaluated against that gallery

The ground_truth field maps each query crater ID to gallery-present relevant crater IDs.

The raw query co-visibility information is preserved separately in metadata/query_relevance.json:

• co_visible_ids_all: all raw co-visible crater IDs from the source annotation
• relevant_gallery_ids: the subset that is present in the released gallery

splits/train.json is intended for supervised or metric-learning experiments. It excludes the full test relevance closure, not just the 1,000 direct query crater IDs.

Official Counts

• test gallery: 25,000 crater IDs / 50,000 images
• test queries: 1,000 crater IDs / 5,000 images
• train gallery: 23,941 crater IDs / 47,882 images
• raw multi-ID query crater IDs: 428
• gallery-present multi-ID query crater IDs: 59

Quick Start

pip install -r requirements.txt
unzip images.zip  # if not already extracted

python examples/eval_timm_global.py \
  --data-root . \
  --split test \
  --model vit_small_patch16_224.dino \
  --pool cls \
  --batch-size 64 \
  --device cuda

The example script:

• loads splits/test.json
• creates a pretrained timm model
• extracts one feature vector per image
• performs cosine retrieval against the released gallery
• reports Recall@1/5/10, mAP, and MRR

It is intentionally simple and meant as a working baseline rather than the fastest possible evaluator.

Manifest Format

Each split JSON has the form:

{
  "split_name": "train or test",
  "version": "release_v1",
  "gallery_images": [
    {
      "image_id": "02-1-002611_2x",
      "path": "images/gallery/02-1-002611_2x.jpg",
      "crater_id": "02-1-002611",
      "view_type": "2x"
    }
  ],
  "query_images": [
    {
      "image_id": "02-1-002927_view_1",
      "query_id": "02-1-002927",
      "path": "images/query/02-1-002927_view_1.jpg",
      "crater_id": "02-1-002927",
      "view": 1,
      "manual_verified": true
    }
  ],
  "ground_truth": {
    "02-1-002927": ["02-1-002927"]
  }
}

Validation

The repository includes one small validation utility:

• scripts/validate_release.py: checks split consistency, relative paths, and train/test separation

To validate the package locally:

python scripts/validate_release.py

Notes

• Image paths in manifests are relative and portable.
• splits/test.json is the official benchmark entrypoint.
• splits/train.json is intended for training or fine-tuning experiments.