Datasets:

jfang
/

CraterBench-R

license: cc-by-4.0
task_categories:
  - image-to-image
  - image-feature-extraction
tags:
  - mars
  - crater
  - retrieval
  - remote-sensing
  - planetary-science
  - vision-transformer
pretty_name: CraterBench-R
size_categories:
  - 10K<n<100K

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.