jfang commited on
Commit
3893463
·
verified ·
1 Parent(s): 59b9ec5

Add splits, metadata, scripts, and documentation

Browse files
.gitignore ADDED
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+ __pycache__/
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+ .DS_Store
3
+ dist/
README.md ADDED
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1
+ ---
2
+ license: cc-by-4.0
3
+ task_categories:
4
+ - image-to-image
5
+ - image-feature-extraction
6
+ tags:
7
+ - mars
8
+ - crater
9
+ - retrieval
10
+ - remote-sensing
11
+ - planetary-science
12
+ - vision-transformer
13
+ pretty_name: CraterBench-R
14
+ size_categories:
15
+ - 10K<n<100K
16
+ ---
17
+
18
+ # CraterBench-R
19
+
20
+ CraterBench-R is an instance-level crater retrieval benchmark built from Mars CTX imagery.
21
+
22
+ This repository contains the released benchmark images, official split files, relevance metadata, and a minimal evaluation example.
23
+
24
+ ## Summary
25
+
26
+ - `25,000` crater identities in the benchmark gallery
27
+ - `50,000` gallery images with two canonical context crops per crater
28
+ - `1,000` held-out query crater identities
29
+ - `5,000` manually verified query images with five views per query crater
30
+ - official `train` and `test` split manifests with relative paths only
31
+
32
+ ## Download
33
+
34
+ ```bash
35
+ # Download the repository
36
+ pip install huggingface_hub
37
+ huggingface-cli download jfang/CraterBench-R --repo-type dataset --local-dir CraterBench-R
38
+ cd CraterBench-R
39
+
40
+ # Extract images
41
+ unzip images.zip
42
+ ```
43
+
44
+ After extraction the directory should contain `images/gallery/` (50,000 JPEGs) and `images/query/` (5,000 JPEGs).
45
+
46
+ ## Repository Layout
47
+
48
+ - `images.zip`: all benchmark images (gallery + query) in a single archive
49
+ - `splits/test.json`: official benchmark split with full gallery plus query set
50
+ - `splits/train.json`: train gallery with the full test relevance closure removed
51
+ - `metadata/query_relevance.json`: raw co-visible crater IDs and gallery-filtered relevance
52
+ - `metadata/stats.json`: release summary statistics
53
+ - `metadata/source/retrieval_ground_truth_raw.csv`: raw query relevance CSV for reference
54
+ - `examples/eval_timm_global.py`: minimal global-descriptor example for any `timm` image model
55
+ - `requirements.txt`: lightweight requirements for the example script
56
+
57
+ ## Split Semantics
58
+
59
+ `splits/test.json` is the official benchmark split and includes both:
60
+
61
+ - the full gallery
62
+ - the query set evaluated against that gallery
63
+
64
+ The `ground_truth` field maps each query crater ID to gallery-present relevant crater IDs.
65
+
66
+ The raw query co-visibility information is preserved separately in `metadata/query_relevance.json`:
67
+
68
+ - `co_visible_ids_all`: all raw co-visible crater IDs from the source annotation
69
+ - `relevant_gallery_ids`: the subset that is present in the released gallery
70
+
71
+ `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.
72
+
73
+ ## Official Counts
74
+
75
+ - test gallery: `25,000` crater IDs / `50,000` images
76
+ - test queries: `1,000` crater IDs / `5,000` images
77
+ - train gallery: `23,941` crater IDs / `47,882` images
78
+ - raw multi-ID query crater IDs: `428`
79
+ - gallery-present multi-ID query crater IDs: `59`
80
+
81
+ ## Quick Start
82
+
83
+ ```bash
84
+ pip install -r requirements.txt
85
+ unzip images.zip # if not already extracted
86
+
87
+ python examples/eval_timm_global.py \
88
+ --data-root . \
89
+ --split test \
90
+ --model vit_small_patch16_224.dino \
91
+ --pool cls \
92
+ --batch-size 64 \
93
+ --device cuda
94
+ ```
95
+
96
+ The example script:
97
+
98
+ - loads `splits/test.json`
99
+ - creates a pretrained `timm` model
100
+ - extracts one feature vector per image
101
+ - performs cosine retrieval against the released gallery
102
+ - reports Recall@1/5/10, mAP, and MRR
103
+
104
+ It is intentionally simple and meant as a working baseline rather than the fastest possible evaluator.
105
+
106
+ ## Manifest Format
107
+
108
+ Each split JSON has the form:
109
+
110
+ ```json
111
+ {
112
+ "split_name": "train or test",
113
+ "version": "release_v1",
114
+ "gallery_images": [
115
+ {
116
+ "image_id": "02-1-002611_2x",
117
+ "path": "images/gallery/02-1-002611_2x.jpg",
118
+ "crater_id": "02-1-002611",
119
+ "view_type": "2x"
120
+ }
121
+ ],
122
+ "query_images": [
123
+ {
124
+ "image_id": "02-1-002927_view_1",
125
+ "query_id": "02-1-002927",
126
+ "path": "images/query/02-1-002927_view_1.jpg",
127
+ "crater_id": "02-1-002927",
128
+ "view": 1,
129
+ "manual_verified": true
130
+ }
131
+ ],
132
+ "ground_truth": {
133
+ "02-1-002927": ["02-1-002927"]
134
+ }
135
+ }
136
+ ```
137
+
138
+ ## Validation
139
+
140
+ The repository includes one small validation utility:
141
+
142
+ - `scripts/validate_release.py`: checks split consistency, relative paths, and train/test separation
143
+
144
+ To validate the package locally:
145
+
146
+ ```bash
147
+ python scripts/validate_release.py
148
+ ```
149
+
150
+ ## Notes
151
+
152
+ - Image paths in manifests are relative and portable.
153
+ - `splits/test.json` is the official benchmark entrypoint.
154
+ - `splits/train.json` is intended for training or fine-tuning experiments.
RELEASE_NOTES.md ADDED
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1
+ # Release Notes
2
+
3
+ ## release_v1
4
+
5
+ This release package is cleaned for public publication.
6
+
7
+ ### Release-facing changes
8
+
9
+ - removed the auxiliary canonical split from the public package
10
+ - removed paper-era legacy manifests from the public package
11
+ - removed machine-local path assumptions from scripts and docs
12
+ - kept only official `train` and `test` split manifests
13
+ - preserved raw query relevance provenance in `metadata/source/`
14
+ - preserved gallery-filtered query relevance in `metadata/query_relevance.json`
15
+ - added a minimal `timm` evaluation example
16
+ - kept only the validation utility needed by end users
17
+
18
+ ### Official counts
19
+
20
+ - train gallery: `47,882` images / `23,941` crater IDs
21
+ - test gallery: `50,000` images / `25,000` crater IDs
22
+ - test query: `5,000` images / `1,000` crater IDs
23
+
24
+ ### Hosting
25
+
26
+ - primary: Hugging Face Datasets (`jfang/CraterBench-R`)
27
+ - all images distributed as a single `images.zip` archive (1.8 GB)
28
+ - extract with `unzip images.zip` to produce `images/gallery/` and `images/query/`
examples/eval_timm_global.py ADDED
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1
+ #!/usr/bin/env python3
2
+ """
3
+ Minimal high-level example: evaluate a pretrained timm model on CraterBench-R
4
+ using one global descriptor per image and cosine retrieval.
5
+ """
6
+
7
+ from __future__ import annotations
8
+
9
+ import argparse
10
+ import json
11
+ from pathlib import Path
12
+ from typing import Any
13
+
14
+ import numpy as np
15
+ import timm
16
+ import torch
17
+ import torch.nn.functional as F
18
+ from PIL import Image
19
+ from timm.data import create_transform, resolve_model_data_config
20
+ from torch.utils.data import DataLoader, Dataset
21
+
22
+
23
+ class ImageDataset(Dataset):
24
+ def __init__(self, entries: list[dict[str, Any]], root: Path, transform):
25
+ self.entries = entries
26
+ self.root = root
27
+ self.transform = transform
28
+
29
+ def __len__(self) -> int:
30
+ return len(self.entries)
31
+
32
+ def __getitem__(self, idx: int):
33
+ entry = self.entries[idx]
34
+ path = self.root / entry["path"]
35
+ image = Image.open(path).convert("RGB")
36
+ return self.transform(image), entry
37
+
38
+
39
+ def collate_with_entries(batch):
40
+ images = torch.stack([item[0] for item in batch])
41
+ entries = [item[1] for item in batch]
42
+ return images, entries
43
+
44
+
45
+ def unwrap_features(features: Any) -> torch.Tensor:
46
+ if isinstance(features, torch.Tensor):
47
+ return features
48
+ if isinstance(features, (list, tuple)):
49
+ return unwrap_features(features[0])
50
+ if isinstance(features, dict):
51
+ for key in ("x", "features", "last_hidden_state"):
52
+ if key in features:
53
+ return unwrap_features(features[key])
54
+ for value in features.values():
55
+ if isinstance(value, torch.Tensor):
56
+ return value
57
+ raise TypeError(f"Unsupported feature output type: {type(features)!r}")
58
+
59
+
60
+ def pool_features(features: torch.Tensor, pool: str) -> torch.Tensor:
61
+ if features.ndim == 2:
62
+ return features
63
+ if features.ndim == 3:
64
+ if pool == "cls":
65
+ return features[:, 0]
66
+ if pool == "mean":
67
+ return features.mean(dim=1)
68
+ if pool == "max":
69
+ return features.max(dim=1).values
70
+ if features.ndim == 4:
71
+ if pool == "mean":
72
+ return features.mean(dim=(2, 3))
73
+ if pool == "max":
74
+ return features.amax(dim=(2, 3))
75
+ raise ValueError(f"Unsupported feature shape {tuple(features.shape)} for pool={pool}")
76
+
77
+
78
+ def extract_embeddings(
79
+ model,
80
+ loader: DataLoader,
81
+ device: torch.device,
82
+ pool: str,
83
+ ) -> tuple[np.ndarray, list[dict[str, Any]]]:
84
+ all_embeddings = []
85
+ all_entries: list[dict[str, Any]] = []
86
+
87
+ model.eval()
88
+ with torch.no_grad():
89
+ for images, entries in loader:
90
+ images = images.to(device)
91
+ features = unwrap_features(model.forward_features(images))
92
+ pooled = pool_features(features, pool)
93
+ pooled = F.normalize(pooled, dim=1)
94
+ all_embeddings.append(pooled.cpu().numpy().astype(np.float32))
95
+ all_entries.extend(entries)
96
+
97
+ return np.concatenate(all_embeddings, axis=0), all_entries
98
+
99
+
100
+ def compute_metrics(
101
+ ranking: np.ndarray,
102
+ query_ids: list[str],
103
+ gallery_ids: list[str],
104
+ ground_truth: dict[str, list[str]],
105
+ k_values: list[int],
106
+ ) -> dict[str, float]:
107
+ metrics: dict[str, float] = {}
108
+ max_k = ranking.shape[1]
109
+
110
+ for k in k_values:
111
+ recalls = []
112
+ for row, query_id in enumerate(query_ids):
113
+ acceptable = set(ground_truth[query_id])
114
+ retrieved = [gallery_ids[idx] for idx in ranking[row, :k]]
115
+ unique_correct = set(retrieved) & acceptable
116
+ recalls.append(min(len(unique_correct) / len(acceptable), 1.0))
117
+ metrics[f"recall@{k}"] = float(np.mean(recalls))
118
+
119
+ aps = []
120
+ reciprocal_ranks = []
121
+ for row, query_id in enumerate(query_ids):
122
+ acceptable = set(ground_truth[query_id])
123
+ retrieved = [gallery_ids[idx] for idx in ranking[row, :max_k]]
124
+
125
+ seen = set()
126
+ precision_at_k = []
127
+ rr = 0.0
128
+ for rank, crater_id in enumerate(retrieved, start=1):
129
+ if crater_id in acceptable and crater_id not in seen:
130
+ seen.add(crater_id)
131
+ precision_at_k.append(len(seen) / rank)
132
+ if rr == 0.0:
133
+ rr = 1.0 / rank
134
+ aps.append(float(np.mean(precision_at_k)) if precision_at_k else 0.0)
135
+ reciprocal_ranks.append(rr)
136
+
137
+ metrics["map"] = float(np.mean(aps))
138
+ metrics["mrr"] = float(np.mean(reciprocal_ranks))
139
+ return metrics
140
+
141
+
142
+ def search_topk(
143
+ query_embeddings: np.ndarray,
144
+ gallery_embeddings: np.ndarray,
145
+ topk: int,
146
+ device: torch.device,
147
+ query_chunk_size: int,
148
+ ) -> np.ndarray:
149
+ gallery = torch.from_numpy(gallery_embeddings).to(device)
150
+ results = []
151
+
152
+ for start in range(0, len(query_embeddings), query_chunk_size):
153
+ end = min(start + query_chunk_size, len(query_embeddings))
154
+ query = torch.from_numpy(query_embeddings[start:end]).to(device)
155
+ scores = query @ gallery.T
156
+ indices = torch.topk(scores, k=topk, dim=1).indices.cpu().numpy()
157
+ results.append(indices)
158
+
159
+ return np.concatenate(results, axis=0)
160
+
161
+
162
+ def main() -> None:
163
+ parser = argparse.ArgumentParser()
164
+ parser.add_argument("--data-root", type=Path, default=Path("."))
165
+ parser.add_argument("--split", type=str, default="test")
166
+ parser.add_argument("--model", type=str, required=True)
167
+ parser.add_argument("--pool", type=str, default="mean", choices=["cls", "mean", "max"])
168
+ parser.add_argument("--batch-size", type=int, default=64)
169
+ parser.add_argument("--query-chunk-size", type=int, default=256)
170
+ parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
171
+ args = parser.parse_args()
172
+
173
+ split_path = args.data_root / "splits" / f"{args.split}.json"
174
+ with split_path.open("r") as handle:
175
+ split = json.load(handle)
176
+
177
+ model = timm.create_model(args.model, pretrained=True, num_classes=0)
178
+ model.to(args.device)
179
+
180
+ data_config = resolve_model_data_config(model)
181
+ transform = create_transform(**data_config, is_training=False)
182
+
183
+ gallery_loader = DataLoader(
184
+ ImageDataset(split["gallery_images"], args.data_root, transform),
185
+ batch_size=args.batch_size,
186
+ shuffle=False,
187
+ num_workers=4,
188
+ pin_memory=True,
189
+ collate_fn=collate_with_entries,
190
+ )
191
+ query_loader = DataLoader(
192
+ ImageDataset(split["query_images"], args.data_root, transform),
193
+ batch_size=args.batch_size,
194
+ shuffle=False,
195
+ num_workers=4,
196
+ pin_memory=True,
197
+ collate_fn=collate_with_entries,
198
+ )
199
+
200
+ gallery_embeddings, gallery_entries = extract_embeddings(
201
+ model, gallery_loader, torch.device(args.device), args.pool
202
+ )
203
+ query_embeddings, query_entries = extract_embeddings(
204
+ model, query_loader, torch.device(args.device), args.pool
205
+ )
206
+
207
+ ranking = search_topk(
208
+ query_embeddings,
209
+ gallery_embeddings,
210
+ topk=10,
211
+ device=torch.device(args.device),
212
+ query_chunk_size=args.query_chunk_size,
213
+ )
214
+
215
+ gallery_ids = [entry["crater_id"] for entry in gallery_entries]
216
+ query_ids = [entry["crater_id"] for entry in query_entries]
217
+ metrics = compute_metrics(ranking, query_ids, gallery_ids, split["ground_truth"], [1, 5, 10])
218
+
219
+ print("Model:", args.model)
220
+ print("Pool:", args.pool)
221
+ for key, value in metrics.items():
222
+ print(f"{key}: {value:.4f}")
223
+
224
+
225
+ if __name__ == "__main__":
226
+ main()
metadata/query_relevance.json ADDED
The diff for this file is too large to render. See raw diff
 
metadata/source/retrieval_ground_truth_raw.csv ADDED
@@ -0,0 +1,1001 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ query_id,acceptable_ids,num_acceptable
2
+ 02-1-002927,02-1-002927,1
3
+ 02-1-002933,02-1-002933,1
4
+ 02-1-003303,02-1-003303,1
5
+ 02-1-003412,02-1-003412,1
6
+ 02-1-003631,02-1-003631,1
7
+ 02-1-003736,02-1-003736,1
8
+ 02-1-003937,02-1-003937,1
9
+ 02-1-004220,02-1-004220,1
10
+ 02-1-004269,02-1-004266;02-1-004267;02-1-004268;02-1-004269;02-1-004295;02-1-004299,6
11
+ 02-1-004297,02-1-004297,1
12
+ 02-1-004400,02-1-004400,1
13
+ 02-1-004972,02-1-004972,1
14
+ 02-1-005002,02-1-005002,1
15
+ 02-1-005076,02-1-005076,1
16
+ 02-1-005205,02-1-005205,1
17
+ 02-1-005214,02-1-005214,1
18
+ 02-1-005282,02-1-005282,1
19
+ 02-1-005330,02-1-005330;02-4-010924,2
20
+ 02-1-005395,02-1-005395,1
21
+ 02-1-005772,02-1-005772,1
22
+ 02-1-005777,02-1-005777,1
23
+ 02-1-006035,02-1-006035;02-1-006036,2
24
+ 02-1-006063,02-1-006063,1
25
+ 02-1-006158,02-1-006158,1
26
+ 02-1-006183,02-1-006183,1
27
+ 02-1-006250,02-1-006250,1
28
+ 02-1-006790,02-1-006789;02-1-006790,2
29
+ 02-1-006806,02-1-006806,1
30
+ 02-1-006879,02-1-006879,1
31
+ 02-1-006913,02-1-006913,1
32
+ 02-1-006980,02-1-006980,1
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947
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948
+ 14-1-009739,14-1-009739,1
949
+ 14-1-010711,14-1-010711;14-1-010722,2
950
+ 14-1-010850,14-1-010850,1
951
+ 14-1-010938,14-1-010938;14-1-010943;14-1-010953,3
952
+ 14-1-011099,14-1-011099,1
953
+ 14-1-011100,14-1-011100;14-1-011103;14-4-005396,3
954
+ 14-1-011314,14-1-011314,1
955
+ 14-1-011503,14-1-011503;14-1-011505;14-1-011506;14-1-011516,4
956
+ 14-1-011730,14-1-011730,1
957
+ 14-1-012027,14-1-012027,1
958
+ 14-1-012210,14-1-012210,1
959
+ 14-1-012285,14-1-012285,1
960
+ 14-1-012452,14-1-012452,1
961
+ 14-1-012474,14-1-012474;14-4-001949,2
962
+ 14-1-012656,14-1-012656,1
963
+ 14-1-012932,14-1-012932;14-1-012942,2
964
+ 14-1-013256,14-1-013256;14-4-001714,2
965
+ 14-1-013440,14-1-013440,1
966
+ 14-1-013663,14-1-013663,1
967
+ 14-2-000197,14-2-000197,1
968
+ 14-2-000238,14-2-000238;14-2-000239,2
969
+ 14-2-000450,14-1-011946;14-1-011961;14-2-000450,3
970
+ 14-4-000276,14-1-013590;14-4-000276,2
971
+ 14-4-000577,14-4-000577;15-1-000836,2
972
+ 14-4-000694,14-4-000694,1
973
+ 14-4-000800,14-1-013251;14-4-000800,2
974
+ 14-4-001518,14-1-013059;14-4-001518,2
975
+ 14-4-001973,14-4-001972;14-4-001973,2
976
+ 14-4-002736,14-4-002736,1
977
+ 14-4-003212,14-4-003211;14-4-003212,2
978
+ 14-4-004107,14-1-011923;14-4-004107,2
979
+ 14-4-004227,14-1-011759;14-4-004227,2
980
+ 14-4-004574,14-4-004574,1
981
+ 14-4-006884,14-4-006884,1
982
+ 14-4-006889,14-4-006889,1
983
+ 14-4-006898,14-4-006898,1
984
+ 14-4-007152,14-1-010722;14-4-007152,2
985
+ 14-4-007181,14-4-007181,1
986
+ 14-4-007191,14-4-007191,1
987
+ 14-4-007441,14-4-007441,1
988
+ 14-4-007796,14-3-000209;14-4-007796,2
989
+ 14-4-008197,14-4-008197,1
990
+ 14-4-008236,14-4-008236,1
991
+ 14-4-008411,14-3-000200;14-4-008411,2
992
+ 14-4-008645,14-4-008645;14-4-008646,2
993
+ 14-4-009463,14-4-009463,1
994
+ 14-4-009549,14-1-009737;14-1-009738;14-4-009549,3
995
+ 14-4-010174,14-4-010174,1
996
+ 14-4-010737,14-4-010737,1
997
+ 14-4-010842,14-4-010842,1
998
+ 14-4-010936,14-4-010936,1
999
+ 14-4-011053,14-4-011053,1
1000
+ 14-4-011300,14-4-011300,1
1001
+ 14-4-011329,14-4-011329,1
metadata/stats.json ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "release_version": "release_v1",
3
+ "official": {
4
+ "gallery_images": 50000,
5
+ "gallery_crater_ids": 25000,
6
+ "query_images": 5000,
7
+ "query_crater_ids": 1000,
8
+ "train_images": 47882,
9
+ "train_crater_ids": 23941,
10
+ "raw_multilabel_query_ids": 428,
11
+ "filtered_multilabel_query_ids": 59,
12
+ "max_filtered_relevant_ids": 3
13
+ }
14
+ }
requirements.txt ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ numpy
2
+ Pillow
3
+ torch
4
+ torchvision
5
+ timm
scripts/validate_release.py ADDED
@@ -0,0 +1,137 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ """
3
+ Validate the public CraterBench-R release package.
4
+ """
5
+
6
+ from __future__ import annotations
7
+
8
+ import json
9
+ import zipfile
10
+ from pathlib import Path
11
+
12
+
13
+ RELEASE_ROOT = Path(__file__).resolve().parents[1]
14
+ LOCAL_STRING_PATTERNS = [
15
+ "/" + "lstr" + "/",
16
+ "mars" + "sim/" + "jfang",
17
+ "z" + "1977143",
18
+ "sa" + "hara" + "/mar" + "ssim",
19
+ ]
20
+
21
+
22
+ def load_json(path: Path) -> dict:
23
+ with path.open("r") as handle:
24
+ return json.load(handle)
25
+
26
+
27
+ def _build_file_index() -> set[str] | None:
28
+ """Return set of image paths from extracted dirs or images.zip."""
29
+ images_dir = RELEASE_ROOT / "images"
30
+ if images_dir.is_dir():
31
+ paths = set()
32
+ for p in images_dir.rglob("*.jpg"):
33
+ paths.add(str(p.relative_to(RELEASE_ROOT)))
34
+ return paths
35
+
36
+ zip_path = RELEASE_ROOT / "images.zip"
37
+ if zip_path.exists():
38
+ with zipfile.ZipFile(zip_path) as zf:
39
+ return set(zf.namelist())
40
+
41
+ return None
42
+
43
+
44
+ def assert_relative_paths(
45
+ entries: list[dict[str, object]], file_index: set[str] | None
46
+ ) -> None:
47
+ for entry in entries:
48
+ relpath = str(entry["path"])
49
+ if Path(relpath).is_absolute():
50
+ raise ValueError(f"Absolute path found in manifest: {relpath}")
51
+ if file_index is not None and relpath not in file_index:
52
+ raise FileNotFoundError(f"Missing file referenced by manifest: {relpath}")
53
+
54
+
55
+ def validate_split_manifests() -> None:
56
+ test_manifest = load_json(RELEASE_ROOT / "splits" / "test.json")
57
+ train_manifest = load_json(RELEASE_ROOT / "splits" / "train.json")
58
+
59
+ file_index = _build_file_index()
60
+ if file_index is None:
61
+ print(" warning: no images/ dir or images.zip found, skipping file checks")
62
+
63
+ assert_relative_paths(test_manifest["gallery_images"], file_index)
64
+ assert_relative_paths(test_manifest["query_images"], file_index)
65
+ assert_relative_paths(train_manifest["gallery_images"], file_index)
66
+
67
+ if train_manifest["query_images"]:
68
+ raise ValueError("Official train split must not contain query images.")
69
+ if train_manifest["ground_truth"]:
70
+ raise ValueError("Official train split must not contain ground truth.")
71
+
72
+ gallery_ids = {str(entry["crater_id"]) for entry in test_manifest["gallery_images"]}
73
+ query_ids = {str(entry["crater_id"]) for entry in test_manifest["query_images"]}
74
+ ground_truth = test_manifest["ground_truth"]
75
+
76
+ if query_ids != set(ground_truth):
77
+ missing = sorted(query_ids - set(ground_truth))
78
+ extra = sorted(set(ground_truth) - query_ids)
79
+ raise ValueError(
80
+ f"Mismatch between query crater IDs and ground truth keys. Missing={missing[:5]} Extra={extra[:5]}"
81
+ )
82
+
83
+ for crater_id, relevant_ids in ground_truth.items():
84
+ if not relevant_ids:
85
+ raise ValueError(f"Empty ground-truth entry for query crater {crater_id}")
86
+ if len(relevant_ids) != len(set(relevant_ids)):
87
+ raise ValueError(f"Duplicate relevant IDs in ground truth for {crater_id}")
88
+ if any(relevant_id not in gallery_ids for relevant_id in relevant_ids):
89
+ raise ValueError(f"Non-gallery relevant ID found in ground truth for {crater_id}")
90
+
91
+ train_ids = {str(entry["crater_id"]) for entry in train_manifest["gallery_images"]}
92
+ excluded_ids = set(ground_truth)
93
+ for relevant_ids in ground_truth.values():
94
+ excluded_ids.update(relevant_ids)
95
+ overlap = train_ids & excluded_ids
96
+ if overlap:
97
+ raise ValueError(
98
+ f"Train split overlaps with test relevance closure: {sorted(overlap)[:10]}"
99
+ )
100
+
101
+
102
+ def scan_for_local_strings() -> None:
103
+ text_files = [
104
+ *RELEASE_ROOT.glob("*.md"),
105
+ *RELEASE_ROOT.glob("*.txt"),
106
+ *RELEASE_ROOT.glob("*.toml"),
107
+ *RELEASE_ROOT.glob("*.yml"),
108
+ *RELEASE_ROOT.glob("*.yaml"),
109
+ *RELEASE_ROOT.glob("scripts/*.py"),
110
+ *RELEASE_ROOT.glob("examples/*.py"),
111
+ *RELEASE_ROOT.glob("splits/*.json"),
112
+ *RELEASE_ROOT.glob("metadata/*.json"),
113
+ ]
114
+
115
+ for path in text_files:
116
+ content = path.read_text()
117
+ for pattern in LOCAL_STRING_PATTERNS:
118
+ if pattern in content:
119
+ raise ValueError(f"Found local string {pattern!r} in {path}")
120
+
121
+
122
+ def main() -> None:
123
+ validate_split_manifests()
124
+ scan_for_local_strings()
125
+
126
+ test_manifest = load_json(RELEASE_ROOT / "splits" / "test.json")
127
+ train_manifest = load_json(RELEASE_ROOT / "splits" / "train.json")
128
+ print("Release package validation passed.")
129
+ print(
130
+ f" test: {len(test_manifest['gallery_images'])} gallery / "
131
+ f"{len(test_manifest['query_images'])} query"
132
+ )
133
+ print(f" train: {len(train_manifest['gallery_images'])} gallery")
134
+
135
+
136
+ if __name__ == "__main__":
137
+ main()
splits/test.json ADDED
The diff for this file is too large to render. See raw diff
 
splits/train.json ADDED
The diff for this file is too large to render. See raw diff