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Implements format-specific serialization via MLCroissant metadata schema, enabling reproducible dataset versioning and cross-framework compatibility. Handles both image and video modalities with configurable compression and encoding options.","intents":["I need to convert this HuggingFace dataset into TFRecord format for TensorFlow training","I want to export the dataset as WebDataset for distributed PyTorch training","I need to create a local LMDB cache for fast repeated evaluation runs"],"best_for":["ML engineers integrating HuggingFace datasets into existing TensorFlow/PyTorch pipelines","teams requiring dataset format standardization across multiple training frameworks","researchers needing reproducible dataset snapshots with MLCroissant metadata"],"limitations":["Format conversion adds 2-4 hours for full 318K image dataset depending on target format","Compression trade-offs: smaller file size (LMDB) vs faster access (uncompressed Parquet)","Video samples require separate handling — frame extraction and codec support varies by format","No built-in deduplication or quality filtering during conversion"],"requires":["datasets library with format-specific backends (tensorflow, webdataset, pyarrow)","Python 3.7+","Sufficient disk space for intermediate conversion (2-3x original dataset size)","Optional: ffmpeg for video frame extraction"],"input_types":["HuggingFace dataset object (ImageFolder format)","format specification (tfrecord, webdataset, parquet, lmdb)","compression configuration (gzip, zstd, none)"],"output_types":["TFRecord shards (.tfrecord files)","WebDataset tar archives (.tar files)","Parquet files with columnar structure","LMDB database directories","MLCroissant metadata JSON"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-merve--vlm_test_images__cap_3","uri":"capability://data.processing.analysis.categorical.image.organization.and.split.management","name":"categorical image organization and split management","description":"Organizes 318K test images into categorical folders (ImageFolder convention) with automatic train/validation/test split inference based on directory structure. Enables programmatic access to category labels, split assignments, and image-to-label mappings through HuggingFace datasets' column-based interface. Supports stratified sampling to maintain category distribution across splits during evaluation.","intents":["I need to access images grouped by category for category-specific VLM evaluation","I want to ensure my evaluation uses balanced category representation across train/val/test","I need to identify which categories my VLM performs poorly on"],"best_for":["researchers analyzing per-category VLM performance metrics","teams building category-aware evaluation dashboards","ML engineers implementing stratified evaluation protocols"],"limitations":["Single-label classification only — no multi-label or hierarchical category support","Category distribution may be imbalanced (unknown from dataset metadata)","No explicit category descriptions or semantic relationships provided","Split assignments are inferred from folder structure — no explicit split metadata"],"requires":["datasets library","Python 3.7+","Knowledge of ImageFolder directory structure"],"input_types":["category name (string)","split specification (train/validation/test)"],"output_types":["filtered dataset subset","category label strings","image counts per category","split distribution statistics"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-merve--vlm_test_images__cap_4","uri":"capability://data.processing.analysis.video.frame.extraction.and.temporal.sampling","name":"video frame extraction and temporal sampling","description":"Extracts individual frames from video samples in the dataset using configurable temporal sampling strategies (uniform, keyframe-based, or random frame selection). Converts video modality samples into image sequences compatible with VLM evaluation pipelines, handling variable frame rates and video durations. Supports batch frame extraction with optional caching to avoid redundant decoding.","intents":["I need to extract frames from video samples for per-frame VLM evaluation","I want to sample key frames from videos to reduce evaluation time","I need to test my VLM's temporal understanding using video frame sequences"],"best_for":["researchers evaluating VLMs on temporal/video understanding tasks","teams building video-to-image conversion pipelines","engineers testing VLM robustness across frame variations"],"limitations":["Frame extraction adds 1-5 seconds per video depending on duration and sampling strategy","No built-in temporal context preservation — frames are treated independently","Video codec support depends on ffmpeg installation and system libraries","Memory overhead for batch frame extraction can exceed single-image evaluation"],"requires":["ffmpeg installed and accessible in PATH","datasets library with video support","Python 3.7+","Optional: opencv-python for advanced frame processing"],"input_types":["video file path (MP4, MOV, WebM formats)","sampling strategy (uniform, keyframe, random)","frame count or sampling interval"],"output_types":["image tensors (PIL Image or NumPy arrays)","frame indices and timestamps","video metadata (duration, fps, resolution)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-merve--vlm_test_images__cap_5","uri":"capability://memory.knowledge.dataset.versioning.and.reproducibility.tracking","name":"dataset versioning and reproducibility tracking","description":"Maintains dataset versioning through HuggingFace Hub's revision system, enabling reproducible evaluation by pinning specific dataset snapshots with commit hashes. Integrates MLCroissant metadata for dataset provenance, including creation date, license information (Apache 2.0), and data source attribution. Supports dataset citation generation for academic publications.","intents":["I need to ensure my VLM evaluation is reproducible across different runs and team members","I want to track which dataset version was used for each model evaluation","I need to cite this dataset properly in my research paper"],"best_for":["academic researchers requiring reproducible evaluation protocols","teams maintaining long-term model evaluation benchmarks","organizations needing audit trails for model validation"],"limitations":["Version history is limited to HuggingFace Hub's retention policy (typically 1 year)","No built-in dataset integrity verification (checksums) — relies on Hub infrastructure","MLCroissant metadata may be incomplete or outdated for older versions","Citation format is limited to BibTeX — no RIS or other formats"],"requires":["HuggingFace account with Hub access","datasets library ≥2.10.0","Python 3.7+"],"input_types":["dataset identifier (merve/vlm_test_images)","revision specification (branch, tag, or commit hash)"],"output_types":["dataset snapshot with pinned version","MLCroissant metadata JSON","BibTeX citation string","commit hash and timestamp"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-merve--vlm_test_images__cap_6","uri":"capability://safety.moderation.apache.2.0.licensed.open.source.dataset.access","name":"apache 2.0 licensed open-source dataset access","description":"Provides unrestricted access to 318K test images under Apache 2.0 license, enabling commercial and research use without licensing restrictions. Hosted on HuggingFace Hub as a public dataset with no authentication barriers for download or streaming. License metadata is embedded in MLCroissant schema for automated compliance checking.","intents":["I need a legally unrestricted dataset for commercial VLM development","I want to ensure my model evaluation dataset has permissive licensing for publication","I need to verify dataset licensing for compliance audits"],"best_for":["commercial teams building VLM products","academic researchers publishing evaluation results","organizations with strict open-source licensing requirements"],"limitations":["Apache 2.0 requires attribution in derivative works — must cite dataset","No warranty or liability guarantees from dataset creators","License compliance is user's responsibility — no automated enforcement","Derivative datasets may have different licensing implications"],"requires":["Acknowledgment of Apache 2.0 license terms","Attribution to original dataset creators (merve)","No additional licensing fees or agreements"],"input_types":["license verification request"],"output_types":["Apache 2.0 license text","attribution requirements","MLCroissant license metadata"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["HuggingFace datasets library (pip install datasets)","Python 3.7+","Internet connection for streaming or ~50-100GB local storage for full download","Vision model inference framework (PyTorch, TensorFlow, or equivalent)","datasets>=2.10.0","Network bandwidth ≥5 Mbps for reasonable streaming performance","HuggingFace account (free) for authenticated access","datasets library with format-specific backends (tensorflow, webdataset, pyarrow)","Sufficient disk space for intermediate conversion (2-3x original dataset size)","Optional: ffmpeg for video frame extraction"],"failure_modes":["Dataset size (318K images) may be insufficient for training large-scale VLMs — better suited for evaluation than pretraining","No explicit metadata annotations provided beyond folder structure — limited for detailed error analysis","ImageFolder format assumes single-label classification; no multi-label or scene-graph annotations","No temporal consistency guarantees for video samples — frame extraction and ordering may vary","Streaming adds ~50-200ms latency per batch fetch depending on network conditions","Random access patterns are slower than sequential iteration due to HTTP range requests","Requires stable internet connection — offline evaluation requires pre-download","Batch prefetching is limited; large batch sizes may cause network bottlenecks","Format conversion adds 2-4 hours for full 318K image dataset depending on target format","Compression trade-offs: smaller file size (LMDB) vs faster access (uncompressed Parquet)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.24,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.764Z","last_scraped_at":"2026-05-03T14:22:48.064Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=merve--vlm_test_images","compare_url":"https://unfragile.ai/compare?artifact=merve--vlm_test_images"}},"signature":"I7q3mnMi47upE/8wuZO+bPifNzmy0wC0SvkgJKPUfIjhHvNm4AqEnOgRd40P0PfqOo7dJl/J3m60Aw/lojtuBw==","signedAt":"2026-06-20T04:01:13.917Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/merve--vlm_test_images","artifact":"https://unfragile.ai/merve--vlm_test_images","verify":"https://unfragile.ai/api/v1/verify?slug=merve--vlm_test_images","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}