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a/mteb/tasks/retrieval/zxx/__init__.py b/mteb/tasks/retrieval/zxx/__init__.py index f7ff3d1801..3628111e0a 100644 --- a/mteb/tasks/retrieval/zxx/__init__.py +++ b/mteb/tasks/retrieval/zxx/__init__.py @@ -1,3 +1,8 @@ +from .fivr_5k_retrieval import ( + FIVR5KCSVRRetrieval, + FIVR5KDSVRRetrieval, + FIVR5KISVRRetrieval, +) from .lp_music_caps import LPMusicCapsMTTA2TRetrieval, LPMusicCapsMTTT2ARetrieval from .music_caps import MusicCapsA2TRetrieval, MusicCapsT2ARetrieval from .song_describer import SongDescriberA2TRetrieval, SongDescriberT2ARetrieval @@ -7,6 +12,9 @@ from .vsc2022_retrieval import VSC2022Retrieval __all__ = [ + "FIVR5KCSVRRetrieval", + "FIVR5KDSVRRetrieval", + "FIVR5KISVRRetrieval", "LPMusicCapsMTTA2TRetrieval", "LPMusicCapsMTTT2ARetrieval", "MusicCapsA2TRetrieval", diff --git a/mteb/tasks/retrieval/zxx/fivr_5k_retrieval.py b/mteb/tasks/retrieval/zxx/fivr_5k_retrieval.py new file mode 100644 index 0000000000..59e886caee --- /dev/null +++ b/mteb/tasks/retrieval/zxx/fivr_5k_retrieval.py @@ -0,0 +1,244 @@ +from __future__ import annotations + +import os +from pathlib import Path +from typing import TYPE_CHECKING, Any, Literal + +from datasets import Dataset, Video, load_dataset + +from mteb.abstasks.retrieval import AbsTaskRetrieval +from mteb.abstasks.retrieval_dataset_loaders import RetrievalSplitData +from mteb.abstasks.task_metadata import TaskMetadata + +if TYPE_CHECKING: + from collections.abc import Sequence + + +_DATASET_PATH = "Cerru02/FIVR-5K-MTEB" +_DATASET_REVISION = "27278cba0b40e0dc764c652efb2e9eed9b997c83" +_CORPUS_SIZE = 3_188 +_QUERY_COUNT = 29 +_VIDEO_SUFFIXES = (".mp4", ".webm", ".mkv", ".mov", ".m4v") +_REGIME = Literal["dsvr", "csvr", "isvr"] + +_BIBTEX = r""" +@article{kordopatis2019fivr, + author = {Kordopatis-Zilos, Giorgos and Papadopoulos, Symeon and Patras, Ioannis and Kompatsiaris, Ioannis}, + journal = {IEEE Transactions on Multimedia}, + title = {FIVR: Fine-grained Incident Video Retrieval}, + year = {2019}, +} + +@inproceedings{kordopatis2019visil, + author = {Kordopatis-Zilos, Giorgos and Papadopoulos, Symeon and Patras, Ioannis and Kompatsiaris, Ioannis}, + booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision}, + title = {ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning}, + year = {2019}, +} + +@article{li2024videoeval, + author = {Li, Xinhao and Huang, Zhenpeng and Wang, Jing and Li, Kunchang and Wang, Limin}, + journal = {arXiv preprint arXiv:2407.06491}, + title = {VideoEval: Comprehensive Benchmark Suite for Low-cost Evaluation of Video Foundation Model}, + year = {2024}, +} +""" + +_REGIME_METADATA = { + "dsvr": { + "name": "FIVR5KDSVRRetrieval", + "definition": "near-duplicate and duplicate-scene videos (ND + DS)", + "prompt": ( + "Retrieve videos that are near-duplicates or show a duplicate scene." + ), + }, + "csvr": { + "name": "FIVR5KCSVRRetrieval", + "definition": ( + "near-duplicate, duplicate-scene, and complementary-scene videos " + "(ND + DS + CS)" + ), + "prompt": ( + "Retrieve videos that are near-duplicates, show a duplicate scene, " + "or show a complementary scene from the same incident." + ), + }, + "isvr": { + "name": "FIVR5KISVRRetrieval", + "definition": ("all videos from the same incident (ND + DS + CS + IS)"), + "prompt": ( + "Retrieve videos from the same incident, including near-duplicates, " + "duplicate scenes, and complementary scenes." + ), + }, +} + + +def _metadata(regime: _REGIME) -> TaskMetadata: + regime_metadata = _REGIME_METADATA[regime] + return TaskMetadata( + name=regime_metadata["name"], + description=( + "Fine-grained incident video retrieval using the established FIVR-5K " + "protocol and VideoEval's frozen manifest. The task retrieves " + f"{regime_metadata['definition']}. The 2026 availability freeze contains " + "29 queries and 3,188 corpus videos; unavailable source media is recorded " + "rather than silently discarded." + ), + reference="https://arxiv.org/abs/1809.04094", + dataset={"path": _DATASET_PATH, "revision": _DATASET_REVISION}, + type="Any2AnyRetrieval", + category="v2v", + modalities=["video"], + eval_splits=["test"], + eval_langs=["zxx-Zxxx"], + main_score=f"map_at_{_CORPUS_SIZE}", + date=("2013-01-01", "2017-12-31"), + domains=["News", "Web"], + task_subtypes=["Duplicate Detection"], + license="not specified", + annotations_creators="human-annotated", + dialect=[], + sample_creation="found", + bibtex_citation=_BIBTEX, + prompt={"query": regime_metadata["prompt"]}, + is_beta=True, + ) + + +def _find_video(video_dir: Path, video_id: str) -> Path | None: + for suffix in _VIDEO_SUFFIXES: + path = video_dir / f"{video_id}{suffix}" + if path.is_file() and path.stat().st_size > 0: + return path + return None + + +def _video_directory(override: str | Path | None = None) -> Path: + configured = override or os.environ.get("MTEB_FIVR_VIDEO_DIR") + if configured is None: + raise ValueError( + "FIVR media is not distributed with the metadata dataset. Prepare a " + "local video directory with scripts/data/fivr/create_data.py, then set " + "MTEB_FIVR_VIDEO_DIR or pass fivr_video_dir to load_data()." + ) + video_dir = Path(configured).expanduser() + if not video_dir.is_dir(): + raise FileNotFoundError(f"FIVR video directory does not exist: {video_dir}") + return video_dir + + +def _resolve_local_videos( + rows: Sequence[dict[str, Any]], video_dir: Path +) -> dict[str, Path]: + paths: dict[str, Path] = {} + missing: list[str] = [] + for row in rows: + video_id = row["id"] + path = _find_video(video_dir, video_id) + if path is None: + missing.append(video_id) + else: + paths[video_id] = path + if missing: + missing_ids = ", ".join(sorted(missing)[:10]) + raise FileNotFoundError( + f"FIVR video directory is missing {len(missing)} frozen videos: " + f"{missing_ids}. The task never downloads media or changes its frozen " + "corpus; prepare the complete directory with " + "scripts/data/fivr/create_data.py and retry." + ) + return paths + + +def _as_video_dataset( + rows: Sequence[dict[str, Any]], paths: dict[str, Path] +) -> Dataset: + return Dataset.from_dict( + { + "id": [row["id"] for row in rows], + "video": [str(paths[row["id"]]) for row in rows], + } + ).cast_column("video", Video()) + + +def _load_fivr( + task: AbsTaskRetrieval, + regime: _REGIME, + *, + num_proc: int | None, + metadata_dir: str | Path | None, + video_dir: str | Path | None, +) -> None: + if task.data_loaded: + return + if metadata_dir is not None: + local_dir = Path(metadata_dir).expanduser() + corpus_metadata = Dataset.from_json(str(local_dir / "corpus.jsonl")) + query_metadata = Dataset.from_json(str(local_dir / "queries.jsonl")) + qrels = Dataset.from_json(str(local_dir / f"{regime}-qrels.jsonl")) + else: + dataset_args = { + "path": task.metadata.dataset["path"], + "revision": task.metadata.dataset["revision"], + "split": "test", + "num_proc": num_proc, + } + corpus_metadata = load_dataset(name="corpus", **dataset_args) + query_metadata = load_dataset(name="queries", **dataset_args) + qrels = load_dataset(name=f"{regime}-qrels", **dataset_args) + if len(corpus_metadata) != _CORPUS_SIZE or len(query_metadata) != _QUERY_COUNT: + raise ValueError("FIVR metadata counts differ from the frozen task definition") + + all_rows = list(corpus_metadata) + list(query_metadata) + paths = _resolve_local_videos(all_rows, _video_directory(video_dir)) + relevant_docs: dict[str, dict[str, int]] = {} + for row in qrels: + relevant_docs.setdefault(row["query-id"], {})[row["corpus-id"]] = row["score"] + if set(relevant_docs) != set(query_metadata["id"]): + raise ValueError("FIVR qrels do not cover the frozen query set") + + task.dataset = { + "default": { + "test": RetrievalSplitData( + corpus=_as_video_dataset(corpus_metadata, paths), + queries=_as_video_dataset(query_metadata, paths), + relevant_docs=relevant_docs, + top_ranked=None, + ) + } + } + task.data_loaded = True + + +class _FIVR5KTaskMixin: + k_values = (1, 3, 5, 10, 20, 100, 1000, _CORPUS_SIZE) + _top_k = _CORPUS_SIZE + _regime: _REGIME + + def load_data(self, num_proc: int | None = None, **kwargs: Any) -> None: + _load_fivr( + self, + self._regime, + num_proc=num_proc, + metadata_dir=( + kwargs.get("fivr_metadata_dir") + or os.environ.get("MTEB_FIVR_METADATA_DIR") + ), + video_dir=kwargs.get("fivr_video_dir"), + ) + + +class FIVR5KDSVRRetrieval(_FIVR5KTaskMixin, AbsTaskRetrieval): + metadata = _metadata("dsvr") + _regime = "dsvr" + + +class FIVR5KCSVRRetrieval(_FIVR5KTaskMixin, AbsTaskRetrieval): + metadata = _metadata("csvr") + _regime = "csvr" + + +class FIVR5KISVRRetrieval(_FIVR5KTaskMixin, AbsTaskRetrieval): + metadata = _metadata("isvr") + _regime = "isvr" diff --git a/scripts/data/fivr/audit_availability.py b/scripts/data/fivr/audit_availability.py new file mode 100644 index 0000000000..bfa9105732 --- /dev/null +++ b/scripts/data/fivr/audit_availability.py @@ -0,0 +1,391 @@ +"""Audit availability of the frozen VideoEval FIVR-5K YouTube manifest. + +The script never downloads media. It resolves each YouTube ID with yt-dlp or +pytubefix, records a structured status, and writes results in manifest order. +The output is intended to be frozen and consumed by ``create_data.py``; +construction must not silently reinterpret a later availability state. + +Example: + uvx --with yt-dlp python scripts/data/fivr/audit_availability.py \ + --manifest VidEB/annotations/FIVR-5K/used_videos.txt \ + --output availability-2026-08-10.json --workers 8 +""" + +from __future__ import annotations + +import argparse +import hashlib +import importlib +import importlib.metadata +import json +import os +import tempfile +import time +from collections import Counter +from concurrent.futures import ThreadPoolExecutor, as_completed +from datetime import UTC, datetime +from pathlib import Path +from typing import Any + + +class _QuietLogger: + def debug(self, message: str) -> None: + pass + + def info(self, message: str) -> None: + pass + + def warning(self, message: str) -> None: + pass + + def error(self, message: str) -> None: + pass + + +def _classify_error(message: str) -> str: + message_lower = message.lower() + patterns = ( + ("private video", "private"), + ("copyright", "copyright_blocked"), + ("removed", "removed"), + ("no longer available", "removed"), + ("video unavailable", "unavailable"), + ("not available", "unavailable"), + ("confirm your age", "age_restricted"), + ("age-restricted", "age_restricted"), + ("members-only", "members_only"), + ("members only", "members_only"), + ("not available in your country", "geo_blocked"), + ("not made this video available in your country", "geo_blocked"), + ("sign in to confirm you’re not a bot", "bot_check"), + ("sign in to confirm you're not a bot", "bot_check"), + ("too many requests", "rate_limited"), + ("http error 429", "rate_limited"), + ("please sign in", "login_required"), + ) + for pattern, status in patterns: + if pattern in message_lower: + return status + return "error" + + +def _load_ytdlp() -> Any: + try: + return importlib.import_module("yt_dlp") + except ImportError as error: + raise RuntimeError( + "The yt-dlp backend requires the optional 'yt-dlp' package. " + "Install it or run with --backend pytubefix." + ) from error + + +def _probe_ytdlp( + video_id: str, + socket_timeout: int, + request_delay: float, + force_ipv4: bool, +) -> dict[str, Any]: + yt_dlp = _load_ytdlp() + if request_delay: + time.sleep(request_delay) + options = { + "extractor_retries": 1, + "fragment_retries": 0, + "noplaylist": True, + "no_warnings": True, + "quiet": True, + "logger": _QuietLogger(), + "retries": 1, + "skip_download": True, + "socket_timeout": socket_timeout, + } + if force_ipv4: + options["source_address"] = "0.0.0.0" + try: + with yt_dlp.YoutubeDL(options) as downloader: + info = downloader.extract_info( + f"https://www.youtube.com/watch?v={video_id}", download=False + ) + if info is None: + return { + "id": video_id, + "available": False, + "status": "error", + "probe_backend": "yt-dlp", + } + return { + "id": video_id, + "available": True, + "status": info.get("availability") or "available", + "duration_seconds": info.get("duration"), + "probe_backend": "yt-dlp", + } + except Exception as error: # yt-dlp exposes several extractor exceptions + message = str(error) + return { + "id": video_id, + "available": False, + "status": _classify_error(message), + "error": message, + "probe_backend": "yt-dlp", + } + + +def _probe_pytubefix(video_id: str, request_delay: float) -> dict[str, Any]: + if request_delay: + time.sleep(request_delay) + try: + pytubefix = importlib.import_module("pytubefix") + + video = pytubefix.YouTube( + f"https://www.youtube.com/watch?v={video_id}", + use_oauth=False, + allow_oauth_cache=False, + ) + duration = video.length + if not video.streams: + raise RuntimeError("no downloadable streams") + return { + "id": video_id, + "available": True, + "status": "public", + "duration_seconds": duration, + "probe_backend": "pytubefix", + } + except Exception as error: + error_type = type(error).__name__ + type_statuses = { + "AccountTerminated": "removed", + "AgeRestrictedError": "age_restricted", + "BotDetection": "bot_check", + "LiveStreamError": "unavailable", + "MembersOnly": "members_only", + "PrivateVideo": "private", + "RecordingUnavailable": "unavailable", + "VideoRegionBlocked": "geo_blocked", + "VideoUnavailable": "unavailable", + } + message = str(error) + return { + "id": video_id, + "available": False, + "status": type_statuses.get(error_type, _classify_error(message)), + "error": f"{error_type}: {message}", + "probe_backend": "pytubefix", + } + + +def _probe( + video_id: str, + socket_timeout: int, + request_delay: float, + force_ipv4: bool, + backend: str, +) -> dict[str, Any]: + if backend == "pytubefix": + return _probe_pytubefix(video_id, request_delay) + return _probe_ytdlp(video_id, socket_timeout, request_delay, force_ipv4) + + +def _write_json(path: Path, payload: dict[str, Any]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile( + "w", encoding="utf-8", dir=path.parent, delete=False + ) as stream: + json.dump(payload, stream, indent=2, sort_keys=True) + stream.write("\n") + temporary_path = Path(stream.name) + os.replace(temporary_path, path) + + +def _public_payload(payload: dict[str, Any]) -> dict[str, Any]: + """Remove verbose extractor errors while preserving every audit decision.""" + public = {key: value for key, value in payload.items() if key != "results"} + public["manifest"] = { + **public["manifest"], + "path": "VidEB/annotations/FIVR-5K/used_videos.txt", + } + backends = sorted( + {result.get("probe_backend", "yt-dlp") for result in payload["results"]} + ) + public["probe"] = { + "method": "multi-pass stream enumeration", + "backends": { + backend: importlib.metadata.version(backend) for backend in backends + }, + } + public["network"] = { + "note": ( + "IPv4 was used for yt-dlp retries; final transient statuses were " + "rechecked serially with a request delay." + ) + } + public["results"] = [ + {key: value for key, value in result.items() if key != "error"} + for result in payload["results"] + ] + return public + + +def _payload( + *, + manifest: Path, + manifest_ids: list[str], + results: dict[str, dict[str, Any]], + checked_at: str, + force_ipv4: bool, + backend: str, +) -> dict[str, Any]: + unique_ids = list(dict.fromkeys(manifest_ids)) + ordered_results = [ + results[video_id] for video_id in unique_ids if video_id in results + ] + statuses = Counter(result["status"] for result in ordered_results) + return { + "schema_version": 1, + "checked_at_utc": checked_at, + "updated_at_utc": datetime.now(UTC).replace(microsecond=0).isoformat(), + "probe": { + "backend": backend, + "version": importlib.metadata.version(backend), + }, + "network": {"force_ipv4": force_ipv4}, + "manifest": { + "path": str(manifest), + "sha256": hashlib.sha256(manifest.read_bytes()).hexdigest(), + "rows": len(manifest_ids), + "unique_ids": len(unique_ids), + "duplicate_ids": sorted( + video_id + for video_id, count in Counter(manifest_ids).items() + if count > 1 + ), + }, + "summary": { + "checked": len(ordered_results), + "available": sum(result["available"] for result in ordered_results), + "unavailable": sum(not result["available"] for result in ordered_results), + "status_counts": dict(sorted(statuses.items())), + }, + "results": ordered_results, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--manifest", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument( + "--public-output", + type=Path, + help="Also write a compact artifact without verbose extractor errors.", + ) + parser.add_argument("--workers", type=int, default=8) + parser.add_argument("--socket-timeout", type=int, default=20) + parser.add_argument( + "--backend", + choices=("yt-dlp", "pytubefix"), + default="yt-dlp", + ) + parser.add_argument( + "--force-ipv4", + action="store_true", + help="Use IPv4, useful when an IPv6 egress is classified as a bot.", + ) + parser.add_argument( + "--request-delay", + type=float, + default=0, + help="Delay each probe to reduce the risk of remote rate limiting.", + ) + parser.add_argument( + "--checkpoint-every", + type=int, + default=25, + help="Atomically refresh the output after this many completed probes.", + ) + parser.add_argument( + "--resume", + action="store_true", + help="Reuse IDs already present in the output file.", + ) + parser.add_argument( + "--retry-status", + action="append", + default=[], + help="When resuming, probe this prior status again (repeatable).", + ) + args = parser.parse_args() + + if args.backend == "yt-dlp": + _load_ytdlp() + + manifest_ids = [ + line.strip() + for line in args.manifest.read_text(encoding="utf-8").splitlines() + if line.strip() + ] + unique_ids = list(dict.fromkeys(manifest_ids)) + results: dict[str, dict[str, Any]] = {} + checked_at = datetime.now(UTC).replace(microsecond=0).isoformat() + if args.resume and args.output.exists(): + previous = json.loads(args.output.read_text(encoding="utf-8")) + retry_statuses = set(args.retry_status) + previous_results = previous.get("results", []) + for result in previous_results: + result.setdefault("probe_backend", "yt-dlp") + results = { + result["id"]: result + for result in previous_results + if result["status"] not in retry_statuses + } + checked_at = previous.get("checked_at_utc", checked_at) + + pending = [video_id for video_id in unique_ids if video_id not in results] + with ThreadPoolExecutor(max_workers=args.workers) as executor: + futures = { + executor.submit( + _probe, + video_id, + args.socket_timeout, + args.request_delay, + args.force_ipv4, + args.backend, + ): video_id + for video_id in pending + } + for completed, future in enumerate(as_completed(futures), start=1): + result = future.result() + results[result["id"]] = result + if completed % args.checkpoint_every == 0: + payload = _payload( + manifest=args.manifest, + manifest_ids=manifest_ids, + results=results, + checked_at=checked_at, + force_ipv4=args.force_ipv4, + backend=args.backend, + ) + _write_json(args.output, payload) + print( + f"checked {len(results)}/{len(unique_ids)}: " + f"{payload['summary']['status_counts']}", + flush=True, + ) + + payload = _payload( + manifest=args.manifest, + manifest_ids=manifest_ids, + results=results, + checked_at=checked_at, + force_ipv4=args.force_ipv4, + backend=args.backend, + ) + _write_json(args.output, payload) + if args.public_output is not None: + _write_json(args.public_output, _public_payload(payload)) + print(json.dumps(payload["summary"], indent=2, sort_keys=True)) + + +if __name__ == "__main__": + main() diff --git a/scripts/data/fivr/availability-2026-08-10.json b/scripts/data/fivr/availability-2026-08-10.json new file mode 100644 index 0000000000..52bb58827f --- /dev/null +++ b/scripts/data/fivr/availability-2026-08-10.json @@ -0,0 +1,23928 @@ +{ + "checked_at_utc": "2026-08-10T15:14:45+00:00", + "manifest": { + "duplicate_ids": [ + "eCrhXArKE24" + ], + "path": "VidEB/annotations/FIVR-5K/used_videos.txt", + "rows": 3446, + "sha256": "b2e6718db1f68ed70cfb485c6134149de618d045caae8e7b0940bf64899da8ec", + "unique_ids": 3445 + }, + "network": { + "note": "IPv4 was used for yt-dlp retries; final transient statuses were rechecked serially with a request delay." + }, + "probe": { + "backends": { + "pytubefix": "10.11.0", + "yt-dlp": "2026.7.4" + }, + "method": "multi-pass stream enumeration" + }, + "results": [ + { + "available": true, + "duration_seconds": 49, + "id": "GJij-4mt6SA", + "probe_backend": "yt-dlp", + "status": "public" + }, + { + "available": true, + "duration_seconds": 30, + "id": "yL1ddCm55y4", + "probe_backend": "yt-dlp", + "status": "public" + }, + { + "available": true, + "duration_seconds": 70, + "id": "3ei-VfSpxvA", + "probe_backend": "yt-dlp", + "status": "public" + }, + { + "available": true, + "duration_seconds": 26, + "id": "2n30dbPBNKE", + "probe_backend": "yt-dlp", + "status": "public" + }, + { 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mode 100644 index 0000000000..456b3ec3a7 --- /dev/null +++ b/scripts/data/fivr/create_data.py @@ -0,0 +1,948 @@ +#!/usr/bin/env python3 +"""Construct the availability-frozen FIVR-5K MTEB metadata dataset. + +The public Hugging Face artifact produced by this script contains identifiers, +source URLs, availability decisions, and qrels. It deliberately does not upload +the underlying YouTube video bytes. With ``--download-media``, the same script +can materialize a local, resumable media cache for validation and evaluation. + +Example: + uvx --with av --with datasets --with huggingface-hub --with pytubefix \ + --with yt-dlp python \ + scripts/data/fivr/create_data.py \ + --fivr-dir /path/to/FIVR-200K \ + --videoeval-dir /path/to/VideoEval \ + --visil-pickle /path/to/visil/datasets/fivr.pickle \ + --availability-audit availability-2026-08-10.json \ + --output-dir /tmp/fivr-mteb \ + --video-dir /tmp/fivr-mteb/videos --download-media +""" + +from __future__ import annotations + +import argparse +import hashlib +import importlib +import json +import os +import pickle +import shutil +import subprocess +import tempfile +from collections import Counter +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path +from typing import Any, cast + +from datasets import Dataset, DatasetDict +from huggingface_hub import HfApi, create_repo + +FIVR_REVISION = "77e3d8a9c159eed9a0b9686e2d62bc7ab3106e24" +VIDEOEVAL_REVISION = "8b86d707ce65ab07323745f7b9669e0d404e11e0" +VISIL_REVISION = "0971e54fb8325fceb1bc9748ecbfe4c66e5dabd2" + +EXPECTED_FILE_SHA256 = { + "fivr_annotation": ( + "ccd343bd1c442142da3b2ef9e571ae888d44ac039fd02de708b0be820385a3ea" + ), + "fivr_ids": ("6fa192644eb84f4a3b88839d850de9d38ac25de90622ccb8cf4a111a75829c06"), + "videoeval_annotation": ( + "ac75b018fad02bdfba233f47bc1c561542309d8b91728ca6dc63f54a164f49ee" + ), + "videoeval_queries": ( + "f98302ab962d5da4c84e52cd0c7670f1b125882052894250efd46578381f43bd" + ), + "videoeval_database": ( + "ac32d2b863580fdf60b7b62da67d9d2f98631f4071d00c889a282865625a96a2" + ), + "videoeval_manifest": ( + "b2e6718db1f68ed70cfb485c6134149de618d045caae8e7b0940bf64899da8ec" + ), + "visil_metadata": ( + "05664821a6f3f5ccdb0cbfbcd26dd712e6aea64f9a1665abe0743110d04e991e" + ), +} + +LABELS = ("ND", "DS", "CS", "IS") +REGIMES = { + "dsvr": ("ND", "DS"), + "csvr": ("ND", "DS", "CS"), + "isvr": ("ND", "DS", "CS", "IS"), +} +VIDEO_SUFFIXES = (".mp4", ".mkv", ".webm", ".mov", ".m4v") + + +class _RestrictedUnpickler(pickle.Unpickler): + """Load ViSiL's primitive-only metadata pickle without arbitrary globals.""" + + def find_class(self, module: str, name: str) -> Any: + if (module, name) == ("builtins", "set"): + return set + raise pickle.UnpicklingError(f"forbidden pickle global: {module}.{name}") + + +class _QuietYtdlpLogger: + def debug(self, message: str) -> None: + pass + + def info(self, message: str) -> None: + pass + + def warning(self, message: str) -> None: + pass + + def error(self, message: str) -> None: + pass + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as stream: + for chunk in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _validate_sha(path: Path, expected: str, label: str) -> None: + actual = _sha256(path) + if actual != expected: + raise ValueError(f"{label} SHA256 mismatch: expected {expected}, got {actual}") + + +def _load_ids(path: Path) -> list[str]: + return [ + line.split()[0].removesuffix(".mp4") + for line in path.read_text(encoding="utf-8").splitlines() + if line.strip() + ] + + +def _normalise_annotation(annotation: dict[str, Any]) -> dict[str, dict[str, set[str]]]: + return { + query_id: {label: set(labels.get(label, [])) for label in LABELS} + for query_id, labels in annotation.items() + } + + +def _load_visil(path: Path) -> dict[str, Any]: + _validate_sha(path, EXPECTED_FILE_SHA256["visil_metadata"], "ViSiL metadata") + with path.open("rb") as stream: + return cast("dict[str, Any]", _RestrictedUnpickler(stream).load()) + + +def _source_paths( + fivr_dir: Path, videoeval_dir: Path +) -> tuple[Path, Path, Path, Path, Path, Path]: + fivr_dataset = fivr_dir / "dataset" + videoeval = videoeval_dir / "VidEB" / "annotations" / "FIVR-5K" + return ( + fivr_dataset / "annotation.json", + fivr_dataset / "youtube_ids.txt", + videoeval / "annotation.json", + videoeval / "queries" / "test.csv", + videoeval / "database" / "test.csv", + videoeval / "used_videos.txt", + ) + + +def _validate_sources( + *, + fivr_dir: Path, + videoeval_dir: Path, + visil_pickle: Path, +) -> dict[str, Any]: + ( + fivr_annotation_path, + fivr_ids_path, + videoeval_annotation_path, + query_path, + database_path, + manifest_path, + ) = _source_paths(fivr_dir, videoeval_dir) + + for path in ( + fivr_annotation_path, + fivr_ids_path, + videoeval_annotation_path, + query_path, + database_path, + manifest_path, + visil_pickle, + ): + if not path.is_file(): + raise FileNotFoundError(path) + + for key, path in ( + ("fivr_annotation", fivr_annotation_path), + ("fivr_ids", fivr_ids_path), + ("videoeval_annotation", videoeval_annotation_path), + ("videoeval_queries", query_path), + ("videoeval_database", database_path), + ("videoeval_manifest", manifest_path), + ): + _validate_sha(path, EXPECTED_FILE_SHA256[key], key) + + fivr_ids = _load_ids(fivr_ids_path) + official_annotation = json.loads(fivr_annotation_path.read_text(encoding="utf-8")) + videoeval_annotation = json.loads( + videoeval_annotation_path.read_text(encoding="utf-8") + ) + if official_annotation != videoeval_annotation: + raise ValueError("VideoEval annotation.json differs from FIVR-200K") + if len(fivr_ids) != 225_960 or len(set(fivr_ids)) != len(fivr_ids): + raise ValueError("unexpected FIVR-200K ID count or duplicate ID") + if len(official_annotation) != 100: + raise ValueError("unexpected FIVR-200K query annotation count") + + queries = _load_ids(query_path) + database = _load_ids(database_path) + manifest_rows = _load_ids(manifest_path) + manifest_ids = list(dict.fromkeys(manifest_rows)) + if len(queries) != 31 or len(database) != 3415: + raise ValueError("unexpected VideoEval FIVR-5K query/database counts") + if len(set(queries)) != len(queries) or len(set(database)) != len(database): + raise ValueError("duplicate ID within VideoEval query or database rows") + if len(manifest_rows) != 3446 or len(manifest_ids) != 3445: + raise ValueError("unexpected VideoEval FIVR-5K manifest counts") + if set(queries) | set(database) != set(manifest_ids): + raise ValueError("VideoEval query/database union differs from used_videos.txt") + if set(queries) & set(database) != {"eCrhXArKE24"}: + raise ValueError("unexpected VideoEval query/database overlap") + if not set(manifest_ids) <= set(fivr_ids): + raise ValueError("VideoEval manifest contains IDs outside FIVR-200K") + if not set(queries) <= set(official_annotation): + raise ValueError("VideoEval query is missing from official annotations") + cross_query_positives = { + (query_id, video_id) + for query_id in queries + for label in LABELS + for video_id in official_annotation[query_id].get(label, []) + if video_id in set(queries) - {query_id} + } + if cross_query_positives: + raise ValueError( + "removing query IDs from the corpus would remove cross-query positives" + ) + + visil = _load_visil(visil_pickle) + canonical_queries = list(visil["5k"]["queries"]) + canonical_database = set(visil["5k"]["database"]) + if len(canonical_queries) != 50 or len(canonical_database) != 5000: + raise ValueError("unexpected canonical ViSiL FIVR-5K counts") + if not set(queries) <= set(canonical_queries): + raise ValueError("VideoEval queries are not a subset of canonical FIVR-5K") + if not set(database) <= canonical_database: + raise ValueError("VideoEval database is not a subset of canonical FIVR-5K") + visil_annotation = _normalise_annotation(visil["annotation"]) + current_annotation = _normalise_annotation(official_annotation) + if set(visil_annotation) != set(current_annotation): + raise ValueError("ViSiL and current FIVR annotations cover different queries") + changed_queries = sorted( + query_id + for query_id in current_annotation + if visil_annotation[query_id] != current_annotation[query_id] + ) + changed_videoeval_queries = sorted(set(changed_queries) & set(queries)) + manifest_set = set(manifest_ids) + manifest_differences = sorted( + (query_id, label, video_id, "visil-only") + for query_id in queries + for label in LABELS + for video_id in ( + visil_annotation[query_id][label] - current_annotation[query_id][label] + ) + if video_id in manifest_set + ) + sorted( + (query_id, label, video_id, "current-only") + for query_id in queries + for label in LABELS + for video_id in ( + current_annotation[query_id][label] - visil_annotation[query_id][label] + ) + if video_id in manifest_set + ) + expected_manifest_differences = [ + ("5MBA_7vDhII", "IS", "Z8ZhfkHyTmQ", "visil-only"), + ("e33dD_qoqhg", "IS", "wcVRzMqpfCg", "visil-only"), + ] + if manifest_differences != expected_manifest_differences: + raise ValueError( + "unexpected annotation drift between ViSiL and current FIVR metadata" + ) + + return { + "annotation": official_annotation, + "fivr_ids": fivr_ids, + "queries": queries, + "database": database, + "manifest_rows": manifest_rows, + "manifest_ids": manifest_ids, + "canonical_queries": canonical_queries, + "canonical_database": canonical_database, + "annotation_drift": { + "changed_queries": len(changed_queries), + "changed_videoeval_queries": len(changed_videoeval_queries), + "manifest_differences": [ + { + "query_id": query_id, + "label": label, + "video_id": video_id, + "direction": direction, + } + for query_id, label, video_id, direction in manifest_differences + ], + }, + } + + +def _load_audit(path: Path, manifest_ids: list[str]) -> dict[str, dict[str, Any]]: + payload = json.loads(path.read_text(encoding="utf-8")) + if ( + payload.get("manifest", {}).get("sha256") + != EXPECTED_FILE_SHA256["videoeval_manifest"] + ): + raise ValueError("availability audit refers to a different manifest") + results = payload.get("results", []) + by_id = {row["id"]: row for row in results} + if set(by_id) != set(manifest_ids): + missing = sorted(set(manifest_ids) - set(by_id)) + extra = sorted(set(by_id) - set(manifest_ids)) + raise ValueError( + f"availability audit is incomplete: missing={missing[:5]} extra={extra[:5]}" + ) + transient = { + row["id"] + for row in results + if row["status"] in {"bot_check", "error", "rate_limited"} + } + if transient: + raise ValueError( + "availability audit contains unresolved transient statuses: " + + ", ".join(sorted(transient)[:10]) + ) + return by_id + + +def _ordered_positives( + annotation: dict[str, Any], query_id: str, labels: tuple[str, ...] +) -> list[str]: + result: list[str] = [] + seen: set[str] = set() + for label in labels: + for video_id in annotation[query_id].get(label, []): + if video_id not in seen: + result.append(video_id) + seen.add(video_id) + return result + + +def _build_rows( + sources: dict[str, Any], audit: dict[str, dict[str, Any]] +) -> dict[str, Any]: + annotation = sources["annotation"] + videoeval_queries = sources["queries"] + videoeval_database = sources["database"] + query_set = set(videoeval_queries) + + # VideoEval includes one query in its database. Its evaluator explicitly + # skips self-matches, so remove every query ID from the MTEB corpus. + original_corpus = [ + video_id for video_id in videoeval_database if video_id not in query_set + ] + available_ids = {video_id for video_id, row in audit.items() if row["available"]} + corpus_ids = [video_id for video_id in original_corpus if video_id in available_ids] + corpus_set = set(corpus_ids) + + query_decisions: list[dict[str, Any]] = [] + retained_queries: list[str] = [] + for query_id in videoeval_queries: + strict_positives = ( + set(_ordered_positives(annotation, query_id, REGIMES["dsvr"])) & corpus_set + ) + query_available = query_id in available_ids + retained = query_available and bool(strict_positives) + if not query_available: + reason = f"query media is {audit[query_id]['status']}" + elif not strict_positives: + reason = "no surviving DSVR positive" + else: + reason = "retained" + retained_queries.append(query_id) + query_decisions.append( + { + "id": query_id, + "availability_status": audit[query_id]["status"], + "surviving_dsvr_positives": len(strict_positives), + "retained": retained, + "reason": reason, + } + ) + + corpus_rows = [ + { + "id": video_id, + "youtube_id": video_id, + "source_url": f"https://www.youtube.com/watch?v={video_id}", + "availability_status": audit[video_id]["status"], + "duration_seconds": audit[video_id].get("duration_seconds"), + } + for video_id in corpus_ids + ] + query_rows = [ + { + "id": video_id, + "youtube_id": video_id, + "source_url": f"https://www.youtube.com/watch?v={video_id}", + "availability_status": audit[video_id]["status"], + "duration_seconds": audit[video_id].get("duration_seconds"), + } + for video_id in retained_queries + ] + + qrels: dict[str, list[dict[str, Any]]] = {} + for regime, labels in REGIMES.items(): + rows: list[dict[str, Any]] = [] + for query_id in retained_queries: + positives = _ordered_positives(annotation, query_id, labels) + rows.extend( + {"query-id": query_id, "corpus-id": corpus_id, "score": 1} + for corpus_id in positives + if corpus_id in corpus_set + ) + qrels[regime] = rows + + role_by_id: dict[str, set[str]] = {} + for video_id in videoeval_queries: + role_by_id.setdefault(video_id, set()).add("query") + for video_id in videoeval_database: + role_by_id.setdefault(video_id, set()).add("database") + availability_rows = [ + { + "id": video_id, + "manifest_role": "+".join(sorted(role_by_id[video_id])), + "available": bool(audit[video_id]["available"]), + "status": audit[video_id]["status"], + "duration_seconds": audit[video_id].get("duration_seconds"), + } + for video_id in sources["manifest_ids"] + ] + + loss_rows: list[dict[str, Any]] = [] + original_corpus_set = set(original_corpus) + for query_id in videoeval_queries: + for label in LABELS: + original = [ + video_id + for video_id in annotation[query_id].get(label, []) + if video_id in original_corpus_set + ] + surviving = [video_id for video_id in original if video_id in corpus_set] + lost = [video_id for video_id in original if video_id not in corpus_set] + loss_rows.append( + { + "query-id": query_id, + "label": label, + "original_positives": len(original), + "surviving_positives": len(surviving), + "lost_positives": len(lost), + "lost_ids": lost, + "lost_statuses": [audit[video_id]["status"] for video_id in lost], + } + ) + + return { + "corpus": corpus_rows, + "queries": query_rows, + "qrels": qrels, + "availability": availability_rows, + "positive_losses": loss_rows, + "query_decisions": query_decisions, + } + + +def _validate_rows(rows: dict[str, Any]) -> None: + corpus_ids = {row["id"] for row in rows["corpus"]} + query_ids = {row["id"] for row in rows["queries"]} + if len(corpus_ids) != len(rows["corpus"]): + raise ValueError("duplicate corpus IDs") + if len(query_ids) != len(rows["queries"]): + raise ValueError("duplicate query IDs") + if corpus_ids & query_ids: + raise ValueError("query IDs must not appear in the corpus") + for regime, qrel_rows in rows["qrels"].items(): + qrel_queries = {row["query-id"] for row in qrel_rows} + if qrel_queries != query_ids: + raise ValueError(f"{regime} qrels do not cover every retained query") + for row in qrel_rows: + if row["corpus-id"] not in corpus_ids: + raise ValueError(f"{regime} qrel references missing corpus media") + if row["score"] != 1: + raise ValueError(f"{regime} contains a non-binary qrel") + qrel_pairs = { + regime: {(row["query-id"], row["corpus-id"]) for row in qrel_rows} + for regime, qrel_rows in rows["qrels"].items() + } + if not qrel_pairs["dsvr"] <= qrel_pairs["csvr"] <= qrel_pairs["isvr"]: + raise ValueError("official DSVR/CSVR/ISVR relevance unions are not nested") + + +def _find_video(video_dir: Path, video_id: str) -> Path | None: + for suffix in VIDEO_SUFFIXES: + candidate = video_dir / f"{video_id}{suffix}" + if candidate.is_file() and candidate.stat().st_size > 0: + return candidate + return None + + +def _video_decodes(path: Path) -> bool: + ffmpeg = shutil.which("ffmpeg") + if ffmpeg is not None: + result = subprocess.run( + [ + ffmpeg, + "-v", + "error", + "-i", + str(path), + "-frames:v", + "1", + "-f", + "null", + "-", + ], + capture_output=True, + text=True, + ) + return result.returncode == 0 + try: + import av + + with av.open(str(path)) as container: + return next(container.decode(video=0), None) is not None + except ImportError: + return path.is_file() and path.stat().st_size > 0 + except Exception: + return False + + +def _download_one( + video_id: str, + video_dir: Path, + resolution: int, + force_ipv4: bool, + backend: str, +) -> dict[str, Any]: + existing = _find_video(video_dir, video_id) + if existing is not None and _video_decodes(existing): + return { + "id": video_id, + "ok": True, + "path": str(existing), + "resumed": True, + "backend": "cache", + } + if existing is not None: + existing.unlink() + + errors: list[str] = [] + if backend in {"auto", "yt-dlp"}: + try: + yt_dlp = importlib.import_module("yt_dlp") + + options: dict[str, Any] = { + "continuedl": True, + "format": ( + f"bestvideo[height<={resolution}]/best[height<={resolution}]/" + "bestvideo/best" + ), + "logger": _QuietYtdlpLogger(), + "noplaylist": True, + "no_warnings": True, + "outtmpl": str(video_dir / f"{video_id}.%(ext)s"), + "quiet": True, + "retries": 3, + } + if force_ipv4: + options["source_address"] = "0.0.0.0" + node = shutil.which("node") + if node is not None: + options["js_runtimes"] = {"node": {"path": node}} + options["remote_components"] = {"ejs:github"} + with yt_dlp.YoutubeDL(options) as downloader: + downloader.download([f"https://www.youtube.com/watch?v={video_id}"]) + path = _find_video(video_dir, video_id) + if path is None or not _video_decodes(path): + raise RuntimeError("downloaded file is missing or undecodable") + return { + "id": video_id, + "ok": True, + "path": str(path), + "resumed": False, + "backend": "yt-dlp", + } + except Exception as error: + errors.append(f"yt-dlp: {error}") + failed_path = _find_video(video_dir, video_id) + if failed_path is not None and not _video_decodes(failed_path): + failed_path.unlink() + if backend == "yt-dlp": + return { + "id": video_id, + "ok": False, + "error": errors[-1][-1000:], + } + + try: + pytubefix = importlib.import_module("pytubefix") + + video = pytubefix.YouTube( + f"https://www.youtube.com/watch?v={video_id}", + use_oauth=False, + allow_oauth_cache=False, + ) + stream_groups = ( + list(video.streams.filter(progressive=True, file_extension="mp4")), + list(video.streams.filter(only_video=True, file_extension="mp4")), + list(video.streams.filter(progressive=True)), + list(video.streams.filter(only_video=True)), + ) + streams = next((group for group in stream_groups if group), []) + eligible = [ + stream + for stream in streams + if stream.resolution is not None + and int(stream.resolution.removesuffix("p")) <= resolution + ] + stream = max( + eligible or streams, + key=lambda item: int((item.resolution or "0p").removesuffix("p")), + default=None, + ) + if stream is None: + raise RuntimeError("no downloadable video stream") + suffix = f".{stream.subtype or 'mp4'}" + path = Path( + stream.download(output_path=video_dir, filename=f"{video_id}{suffix}") + ) + if not _video_decodes(path): + raise RuntimeError("downloaded file is undecodable") + return { + "id": video_id, + "ok": True, + "path": str(path), + "resumed": False, + "backend": "pytubefix", + } + except Exception as error: + for partial in video_dir.glob(f"{video_id}.*"): + if partial.suffix in VIDEO_SUFFIXES and not _video_decodes(partial): + partial.unlink() + errors.append(f"pytubefix: {error}") + return {"id": video_id, "ok": False, "error": "; ".join(errors)[-1000:]} + + +def _materialize_media( + *, + rows: dict[str, Any], + video_dir: Path, + workers: int, + resolution: int, + force_ipv4: bool, + backend: str, + output_dir: Path, +) -> None: + video_dir.mkdir(parents=True, exist_ok=True) + ids = list( + dict.fromkeys( + [row["id"] for row in rows["queries"]] + + [row["id"] for row in rows["corpus"]] + ) + ) + results: dict[str, dict[str, Any]] = {} + with ThreadPoolExecutor(max_workers=max(1, workers)) as executor: + futures = { + executor.submit( + _download_one, + video_id, + video_dir, + resolution, + force_ipv4, + backend, + ): video_id + for video_id in ids + } + for completed, future in enumerate(as_completed(futures), start=1): + result = future.result() + results[result["id"]] = result + if completed % 50 == 0: + print(f"media {completed}/{len(ids)}", flush=True) + + ordered = [results[video_id] for video_id in ids] + report = { + "requested": len(ids), + "successful": sum(row["ok"] for row in ordered), + "failed": sum(not row["ok"] for row in ordered), + "resolution": resolution, + "force_ipv4": force_ipv4, + "requested_backend": backend, + "results": ordered, + } + _write_json(output_dir / "download-report.json", report) + failures = [row for row in ordered if not row["ok"]] + if failures: + raise RuntimeError( + f"{len(failures)} frozen videos failed to download; see download-report.json" + ) + + +def _summary(sources: dict[str, Any], rows: dict[str, Any]) -> dict[str, Any]: + status_counts = Counter(row["status"] for row in rows["availability"]) + + def aggregate_losses(query_ids: set[str] | None = None) -> dict[str, Any]: + selected = [ + row + for row in rows["positive_losses"] + if query_ids is None or row["query-id"] in query_ids + ] + return { + "by_label": { + label: { + "original_positives": sum( + row["original_positives"] + for row in selected + if row["label"] == label + ), + "surviving_positives": sum( + row["surviving_positives"] + for row in selected + if row["label"] == label + ), + "lost_positives": sum( + row["lost_positives"] + for row in selected + if row["label"] == label + ), + } + for label in LABELS + }, + "affected_queries": len( + {row["query-id"] for row in selected if row["lost_positives"]} + ), + } + + retained_query_ids = {row["id"] for row in rows["queries"]} + qrel_stats: dict[str, Any] = {} + for regime, qrel_rows in rows["qrels"].items(): + per_query = Counter(row["query-id"] for row in qrel_rows) + counts = list(per_query.values()) + qrel_stats[regime] = { + "qrels": len(qrel_rows), + "min_per_query": min(counts), + "mean_per_query": sum(counts) / len(counts), + "max_per_query": max(counts), + } + return { + "source_revisions": { + "fivr_200k": FIVR_REVISION, + "videoeval": VIDEOEVAL_REVISION, + "visil": VISIL_REVISION, + }, + "canonical_fivr5k": { + "queries": len(sources["canonical_queries"]), + "database": len(sources["canonical_database"]), + }, + "videoeval_fivr5k": { + "queries": len(sources["queries"]), + "database_rows": len(sources["database"]), + "manifest_rows": len(sources["manifest_rows"]), + "manifest_unique_ids": len(sources["manifest_ids"]), + "annotation_drift_from_visil_release": sources["annotation_drift"], + }, + "availability": { + "available": sum(row["available"] for row in rows["availability"]), + "unavailable": sum(not row["available"] for row in rows["availability"]), + "status_counts": dict(sorted(status_counts.items())), + }, + "positive_losses": { + "all_videoeval_queries": aggregate_losses(), + "retained_mteb_queries": aggregate_losses(retained_query_ids), + }, + "mteb": { + "queries": len(rows["queries"]), + "corpus": len(rows["corpus"]), + "qrels": qrel_stats, + "dropped_queries": [ + row for row in rows["query_decisions"] if not row["retained"] + ], + }, + } + + +def _write_json(path: Path, payload: Any) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile( + "w", encoding="utf-8", dir=path.parent, delete=False + ) as stream: + json.dump(payload, stream, indent=2, sort_keys=True) + stream.write("\n") + temporary = Path(stream.name) + os.replace(temporary, path) + + +def _write_jsonl(path: Path, rows: list[dict[str, Any]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8") as stream: + for row in rows: + stream.write(json.dumps(row, sort_keys=True) + "\n") + + +def _write_artifacts( + output_dir: Path, rows: dict[str, Any], summary: dict[str, Any] +) -> None: + output_dir.mkdir(parents=True, exist_ok=True) + _write_jsonl(output_dir / "corpus.jsonl", rows["corpus"]) + _write_jsonl(output_dir / "queries.jsonl", rows["queries"]) + for regime, qrels in rows["qrels"].items(): + _write_jsonl(output_dir / f"{regime}-qrels.jsonl", qrels) + _write_jsonl(output_dir / "availability.jsonl", rows["availability"]) + _write_jsonl(output_dir / "positive-losses.jsonl", rows["positive_losses"]) + _write_jsonl(output_dir / "query-decisions.jsonl", rows["query_decisions"]) + _write_json(output_dir / "summary.json", summary) + + +def _render_card(template_path: Path, summary: dict[str, Any]) -> str: + mteb = summary["mteb"] + availability = summary["availability"] + all_losses = summary["positive_losses"]["all_videoeval_queries"]["by_label"] + retained_losses = summary["positive_losses"]["retained_mteb_queries"]["by_label"] + replacements = { + "{corpus}": mteb["corpus"], + "{queries}": mteb["queries"], + "{available}": availability["available"], + "{unavailable}": availability["unavailable"], + "{dsvr_qrels}": mteb["qrels"]["dsvr"]["qrels"], + "{csvr_qrels}": mteb["qrels"]["csvr"]["qrels"], + "{isvr_qrels}": mteb["qrels"]["isvr"]["qrels"], + "{all_nd_lost}": all_losses["ND"]["lost_positives"], + "{all_ds_lost}": all_losses["DS"]["lost_positives"], + "{all_cs_lost}": all_losses["CS"]["lost_positives"], + "{all_is_lost}": all_losses["IS"]["lost_positives"], + "{retained_nd_lost}": retained_losses["ND"]["lost_positives"], + "{retained_ds_lost}": retained_losses["DS"]["lost_positives"], + "{retained_cs_lost}": retained_losses["CS"]["lost_positives"], + "{retained_is_lost}": retained_losses["IS"]["lost_positives"], + } + card = template_path.read_text(encoding="utf-8") + for placeholder, value in replacements.items(): + card = card.replace(placeholder, str(value)) + return card + + +def _push( + *, + repo_id: str, + rows: dict[str, Any], + output_dir: Path, + card: str, +) -> str: + create_repo(repo_id, repo_type="dataset", exist_ok=True) + configs = { + "corpus": rows["corpus"], + "queries": rows["queries"], + "dsvr-qrels": rows["qrels"]["dsvr"], + "csvr-qrels": rows["qrels"]["csvr"], + "isvr-qrels": rows["qrels"]["isvr"], + "availability": rows["availability"], + "positive-losses": rows["positive_losses"], + "query-decisions": rows["query_decisions"], + } + for config, config_rows in configs.items(): + DatasetDict({"test": Dataset.from_list(config_rows)}).push_to_hub( + repo_id, config + ) + api = HfApi() + api.upload_file( + path_or_fileobj=card.encode(), + path_in_repo="README.md", + repo_id=repo_id, + repo_type="dataset", + commit_message="Document FIVR-5K MTEB metadata", + ) + for filename in ("summary.json", "availability.jsonl", "positive-losses.jsonl"): + api.upload_file( + path_or_fileobj=output_dir / filename, + path_in_repo=f"audit/{filename}", + repo_id=repo_id, + repo_type="dataset", + commit_message=f"Add {filename}", + ) + revision = api.dataset_info(repo_id).sha + if revision is None: + raise RuntimeError("Hugging Face did not return an immutable revision") + (output_dir / "hub-revision.txt").write_text(revision + "\n", encoding="utf-8") + return revision + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--fivr-dir", type=Path, required=True) + parser.add_argument("--videoeval-dir", type=Path, required=True) + parser.add_argument("--visil-pickle", type=Path, required=True) + parser.add_argument( + "--availability-audit", + type=Path, + default=Path(__file__).with_name("availability-2026-08-10.json"), + ) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--video-dir", type=Path) + parser.add_argument("--download-media", action="store_true") + parser.add_argument("--workers", type=int, default=4) + parser.add_argument("--resolution", type=int, default=480) + parser.add_argument( + "--download-backend", + choices=("auto", "yt-dlp", "pytubefix"), + default="auto", + ) + parser.add_argument("--force-ipv4", action="store_true") + parser.add_argument("--repo-id", default="Cerru02/FIVR-5K-MTEB") + parser.add_argument("--push", action="store_true") + args = parser.parse_args() + + sources = _validate_sources( + fivr_dir=args.fivr_dir, + videoeval_dir=args.videoeval_dir, + visil_pickle=args.visil_pickle, + ) + audit = _load_audit(args.availability_audit, sources["manifest_ids"]) + rows = _build_rows(sources, audit) + _validate_rows(rows) + summary = _summary(sources, rows) + _write_artifacts(args.output_dir, rows, summary) + + if args.download_media: + if args.video_dir is None: + raise ValueError("--video-dir is required with --download-media") + _materialize_media( + rows=rows, + video_dir=args.video_dir, + workers=args.workers, + resolution=args.resolution, + force_ipv4=args.force_ipv4, + backend=args.download_backend, + output_dir=args.output_dir, + ) + + print(json.dumps(summary, indent=2, sort_keys=True)) + if args.push: + card_template = Path(__file__).with_name("dataset_card.md") + revision = _push( + repo_id=args.repo_id, + rows=rows, + output_dir=args.output_dir, + card=_render_card(card_template, summary), + ) + print(f"Pushed {args.repo_id} @ {revision}") + + +if __name__ == "__main__": + main() diff --git a/scripts/data/fivr/dataset_card.md b/scripts/data/fivr/dataset_card.md new file mode 100644 index 0000000000..b83945138c --- /dev/null +++ b/scripts/data/fivr/dataset_card.md @@ -0,0 +1,151 @@ +--- +license: apache-2.0 +pretty_name: FIVR-5K MTEB Metadata +task_categories: + - feature-extraction +tags: + - mteb + - moeb + - video-retrieval + - fivr +configs: + - config_name: corpus + data_files: + - split: test + path: corpus/test-* + - config_name: queries + data_files: + - split: test + path: queries/test-* + - config_name: dsvr-qrels + data_files: + - split: test + path: dsvr-qrels/test-* + - config_name: csvr-qrels + data_files: + - split: test + path: csvr-qrels/test-* + - config_name: isvr-qrels + data_files: + - split: test + path: isvr-qrels/test-* + - config_name: availability + data_files: + - split: test + path: availability/test-* + - config_name: positive-losses + data_files: + - split: test + path: positive-losses/test-* + - config_name: query-decisions + data_files: + - split: test + path: query-decisions/test-* +--- + +# FIVR-5K for MTEB + +This repository packages the metadata needed for the MTEB/MOEB version of +fine-grained incident video retrieval. It contains no video bytes. + +## Relationship to the source benchmarks + +[FIVR-200K](https://github.com/MKLab-ITI/FIVR-200K) is the authoritative +source of the YouTube IDs and human ND/DS/CS/IS annotations. The canonical +FIVR-5K protocol published with +[ViSiL](https://github.com/MKLab-ITI/visil) selects the 50 most difficult DSVR +queries (using iMAC to measure difficulty), randomly retains 30% of annotated +videos per label category for each query, and publishes a 5,000-video database +from FIVR-200K. The released ViSiL metadata is used to validate membership; the +MTEB construction does not rerun that historical random selection. + +[VideoEval](https://huggingface.co/datasets/lixinhao/VideoEval) freezes a +downloadable subset of that established protocol: 31 of the canonical queries, +3,415 canonical database rows, and 3,445 unique IDs (one query is also a +database row). Its annotations are identical to the official FIVR-200K JSON. +The MTEB construction validates all of these relationships instead of sampling +a new subset. + +The older ViSiL pickle and current FIVR/VideoEval annotations differ for two +manifest items, both historical IS positives that are absent from the current +official annotation. The construction records and validates this drift, then +uses the current official annotation bundled identically by VideoEval. + +The MTEB freeze re-audited all VideoEval IDs on 2026-08-10. It observed +{available} available and {unavailable} unavailable/restricted IDs. After +removing unavailable media, excluding the single query self-match from the +corpus, and retaining only available queries with a surviving DSVR positive, +the evaluation has {queries} queries and {corpus} corpus videos. +Across all 31 VideoEval source queries, unavailable corpus media removed +{all_nd_lost} ND, {all_ds_lost} DS, {all_cs_lost} CS, and {all_is_lost} IS +positive assignments. For the {queries} retained evaluation queries, the +corresponding losses are {retained_nd_lost} ND, {retained_ds_lost} DS, +{retained_cs_lost} CS, and {retained_is_lost} IS. The per-query records remain +available in `positive-losses` rather than being silently discarded. + +## Retrieval definitions + +All qrels are binary, exactly following the official evaluator: + +- DSVR: ND + DS ({dsvr_qrels} qrels) +- CSVR: ND + DS + CS ({csvr_qrels} qrels) +- ISVR: ND + DS + CS + IS ({isvr_qrels} qrels) + +The source metric is full-ranking mean average precision. MTEB evaluates +`map_at_`, which is equivalent because every corpus item is ranked. + +## Media and licensing + +The FIVR and ViSiL repositories, the VideoEval metadata repository, and this +metadata-only derivative are Apache-2.0. That license does not grant rights to +redistribute the underlying third-party YouTube videos. VideoEval likewise +instructs users to download original media separately because of potential +copyright issues. + +Consequently, this repository publishes only IDs, original-source URLs, +availability decisions, and qrels. The MTEB task does not download media. +Prepare a complete local video directory separately with the reproducible +construction script, then set `MTEB_FIVR_VIDEO_DIR` or pass `fivr_video_dir` to +the task. Missing files are reported as an error; the benchmark never silently +changes its frozen corpus. + +Users are responsible for complying with the original platforms' terms and +applicable law. Individual media rights remain with their respective owners. + +## Configurations + +- `corpus`, `queries`: identifiers, source URLs, frozen availability status, + and duration metadata; +- `dsvr-qrels`, `csvr-qrels`, `isvr-qrels`: official binary relevance unions; +- `availability`: every ID from VideoEval's frozen manifest, including missing + items; +- `positive-losses`: lost positives per query and ND/DS/CS/IS label; +- `query-decisions`: retained/dropped query decisions. + +The `audit/` directory contains the human-readable construction summary and +the full frozen availability/loss records. + +## Citations + +```bibtex +@article{kordopatis2019fivr, + author = {Kordopatis-Zilos, Giorgos and Papadopoulos, Symeon and Patras, Ioannis and Kompatsiaris, Ioannis}, + journal = {IEEE Transactions on Multimedia}, + title = {FIVR: Fine-grained Incident Video Retrieval}, + year = {2019} +} + +@inproceedings{kordopatis2019visil, + author = {Kordopatis-Zilos, Giorgos and Papadopoulos, Symeon and Patras, Ioannis and Kompatsiaris, Ioannis}, + booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision}, + title = {ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning}, + year = {2019} +} + +@article{li2024videoeval, + author = {Li, Xinhao and Huang, Zhenpeng and Wang, Jing and Li, Kunchang and Wang, Limin}, + journal = {arXiv preprint arXiv:2407.06491}, + title = {VideoEval: Comprehensive Benchmark Suite for Low-cost Evaluation of Video Foundation Model}, + year = {2024} +} +``` diff --git a/scripts/data/fivr/run_shared_evaluation.py b/scripts/data/fivr/run_shared_evaluation.py new file mode 100644 index 0000000000..aa0153cf78 --- /dev/null +++ b/scripts/data/fivr/run_shared_evaluation.py @@ -0,0 +1,299 @@ +#!/usr/bin/env python3 +"""Evaluate all FIVR regimes while encoding their shared corpus only once. + +The three tasks have identical corpus media but different query instructions and +qrels. This runner checkpoints corpus embedding shards, encodes each query view +with its own task prompt, and uses MTEB's retrieval scorer unchanged. + +Example: + MTEB_FIVR_VIDEO_DIR=/path/to/fivr/videos python \ + scripts/data/fivr/run_shared_evaluation.py \ + --model Qwen/Qwen3-VL-Embedding-2B --device mps \ + --num-frames 16 --batch-size 2 --output /tmp/fivr-qwen.json +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import os +import tempfile +from pathlib import Path +from typing import TYPE_CHECKING, Any, cast + +import mteb +import numpy as np +import torch + +from mteb._create_dataloaders import create_dataloader +from mteb._evaluators.retrieval_metrics import ( + calculate_retrieval_scores, + make_score_dict, +) +from mteb.tasks.retrieval.zxx.fivr_5k_retrieval import ( + FIVR5KCSVRRetrieval, + FIVR5KDSVRRetrieval, + FIVR5KISVRRetrieval, +) +from mteb.types import PromptType + +if TYPE_CHECKING: + from collections.abc import Sequence + + from datasets import Dataset + + from mteb.abstasks.retrieval import AbsTaskRetrieval + from mteb.models import EncoderProtocol + + +TASK_CLASSES = ( + FIVR5KDSVRRetrieval, + FIVR5KCSVRRetrieval, + FIVR5KISVRRetrieval, +) + + +def _write_json(path: Path, value: Any) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile( + "w", encoding="utf-8", dir=path.parent, delete=False + ) as stream: + json.dump(value, stream, indent=2, sort_keys=True) + stream.write("\n") + temporary = Path(stream.name) + os.replace(temporary, path) + + +def _write_npy(path: Path, value: np.ndarray) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile("wb", dir=path.parent, delete=False) as stream: + np.save(stream, value) + temporary = Path(stream.name) + os.replace(temporary, path) + + +def _as_numpy(value: Any) -> np.ndarray: + if isinstance(value, torch.Tensor): + return value.detach().cpu().numpy() + return np.asarray(value) + + +def _instruction( + model: EncoderProtocol, task: AbsTaskRetrieval, prompt_type: PromptType +) -> str | None: + getter = getattr(model, "get_task_instruction", None) + return getter(task.metadata, prompt_type) if getter else None + + +def _encode( + model: EncoderProtocol, + task: AbsTaskRetrieval, + dataset: Dataset, + *, + prompt_type: PromptType, + batch_size: int, +) -> np.ndarray: + dataloader = create_dataloader( + dataset, + task_metadata=task.metadata, + prompt_type=prompt_type, + batch_size=batch_size, + ) + return _as_numpy( + model.encode( + dataloader, + task_metadata=task.metadata, + hf_split="test", + hf_subset="default", + prompt_type=prompt_type, + batch_size=batch_size, + show_progress_bar=False, + ) + ) + + +def _encode_checkpointed( + model: EncoderProtocol, + task: AbsTaskRetrieval, + dataset: Dataset, + *, + prompt_type: PromptType, + batch_size: int, + chunk_size: int, + shard_dir: Path, +) -> np.ndarray: + shards: list[np.ndarray] = [] + for start in range(0, len(dataset), chunk_size): + stop = min(start + chunk_size, len(dataset)) + shard_path = shard_dir / f"{start:06d}-{stop:06d}.npy" + if shard_path.is_file(): + shard = np.load(shard_path) + if len(shard) != stop - start: + raise ValueError(f"invalid cached embedding shard: {shard_path}") + else: + shard = _encode( + model, + task, + dataset.select(range(start, stop)), + prompt_type=prompt_type, + batch_size=batch_size, + ) + _write_npy(shard_path, shard) + shards.append(shard) + print(f"encoded {stop}/{len(dataset)}", flush=True) + return np.concatenate(shards) + + +def _score( + task: AbsTaskRetrieval, + query_embeddings: np.ndarray, + corpus_embeddings: np.ndarray, + model: EncoderProtocol, +) -> dict[str, Any]: + split = task.dataset["default"]["test"] + similarities = _as_numpy(model.similarity(query_embeddings, corpus_embeddings)) + query_ids = split["queries"]["id"] + corpus_ids = split["corpus"]["id"] + results = { + query_id: dict(zip(corpus_ids, row.astype(float), strict=True)) + for query_id, row in zip(query_ids, similarities, strict=True) + } + metrics = calculate_retrieval_scores(results, split["relevant_docs"], task.k_values) + return make_score_dict( + ndcg=metrics.ndcg, + _map=metrics.map, + recall=metrics.recall, + precision=metrics.precision, + mrr=metrics.mrr, + naucs=metrics.naucs, + naucs_mrr=metrics.naucs_mrr, + hit_rate=metrics.hit_rate, + task_scores={}, + ) + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--model", required=True) + parser.add_argument("--device") + parser.add_argument("--num-frames", type=int, default=16) + parser.add_argument("--batch-size", type=int, default=2) + parser.add_argument("--chunk-size", type=int, default=64) + parser.add_argument("--metadata-dir", type=Path) + parser.add_argument("--video-dir", type=Path) + parser.add_argument("--embedding-dir", type=Path, default=Path(".fivr-embeddings")) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + + if args.video_dir is None: + configured_video_dir = os.environ.get("MTEB_FIVR_VIDEO_DIR") + if configured_video_dir is None: + raise ValueError("set --video-dir or MTEB_FIVR_VIDEO_DIR") + args.video_dir = Path(configured_video_dir) + + tasks = [task_class() for task_class in TASK_CLASSES] + for task in tasks: + task.load_data( + fivr_metadata_dir=args.metadata_dir, + fivr_video_dir=args.video_dir, + ) + first_split = tasks[0].dataset["default"]["test"] + corpus_ids = list(first_split["corpus"]["id"]) + query_ids = list(first_split["queries"]["id"]) + for task in tasks[1:]: + split = task.dataset["default"]["test"] + if ( + list(split["corpus"]["id"]) != corpus_ids + or list(split["queries"]["id"]) != query_ids + ): + raise ValueError("FIVR task views do not share identical media/order") + + model = cast( + "EncoderProtocol", + mteb.get_model( + args.model, + device=args.device, + fps=None, + num_frames=args.num_frames, + ), + ) + model_meta = model.mteb_model_meta + if model_meta is None: + raise ValueError("model has no MTEB metadata") + document_instructions = [ + _instruction(model, task, PromptType.document) for task in tasks + ] + if len(set(document_instructions)) != 1: + raise ValueError("shared corpus has task-dependent document instructions") + + cache_spec = { + "model": model_meta.name, + "revision": model_meta.revision, + "num_frames": args.num_frames, + "document_instruction": document_instructions[0], + "corpus_ids": corpus_ids, + } + cache_key = hashlib.sha256( + json.dumps(cache_spec, sort_keys=True).encode() + ).hexdigest()[:16] + cache_dir = args.embedding_dir / cache_key + _write_json(cache_dir / "spec.json", cache_spec) + corpus_embeddings = _encode_checkpointed( + model, + tasks[0], + first_split["corpus"], + prompt_type=PromptType.document, + batch_size=args.batch_size, + chunk_size=args.chunk_size, + shard_dir=cache_dir / "corpus", + ) + + scores: dict[str, Any] = {} + query_instructions: dict[str, str | None] = {} + for task in tasks: + query_instruction = _instruction(model, task, PromptType.query) + query_instructions[task.metadata.name] = query_instruction + query_cache_spec = { + "task": task.metadata.name, + "instruction": query_instruction, + "query_ids": query_ids, + } + query_key = hashlib.sha256( + json.dumps(query_cache_spec, sort_keys=True).encode() + ).hexdigest()[:16] + query_path = cache_dir / f"queries-{query_key}.npy" + if query_path.is_file(): + query_embeddings = np.load(query_path) + else: + query_embeddings = _encode( + model, + task, + task.dataset["default"]["test"]["queries"], + prompt_type=PromptType.query, + batch_size=args.batch_size, + ) + _write_npy(query_path, query_embeddings) + scores[task.metadata.name] = _score( + task, query_embeddings, corpus_embeddings, model + ) + + payload = { + "model": model_meta.name, + "revision": model_meta.revision, + "experiment_kwargs": model_meta.experiment_kwargs, + "device": args.device, + "num_frames": args.num_frames, + "batch_size": args.batch_size, + "corpus_size": len(corpus_ids), + "queries": len(query_ids), + "document_instruction": document_instructions[0], + "query_instructions": query_instructions, + "scores": scores, + } + _write_json(args.output, payload) + print(json.dumps(payload, indent=2, sort_keys=True)) + + +if __name__ == "__main__": + main()