{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-img2dataset","slug":"pypi-img2dataset","name":"img2dataset","type":"repo","url":"https://github.com/rom1504/img2dataset","page_url":"https://unfragile.ai/pypi-img2dataset","categories":["model-training"],"tags":["machine","learning","computer","vision","download","image","dataset"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-img2dataset__cap_0","uri":"capability://data.processing.analysis.multi.format.url.list.parsing.and.metadata.extraction","name":"multi-format url list parsing and metadata extraction","description":"The Reader component parses input URL lists from multiple formats (CSV, JSON, JSONL, Parquet) and extracts associated metadata like captions, alt text, and image attributes. It uses temporary feather files for memory-efficient handling of large datasets, sharding the input into work units that can be distributed across workers. This design allows processing of datasets ranging from thousands to billions of images without loading entire datasets into memory.","intents":["I need to convert a CSV of image URLs with captions into a structured dataset","I want to process a billion-row Parquet file of image URLs without running out of RAM","I need to extract and preserve metadata alongside image downloads"],"best_for":["ML engineers building large-scale vision datasets","researchers working with web-scraped image collections","teams migrating from manual dataset curation to automated pipelines"],"limitations":["Requires input URLs to be in supported formats; custom formats need preprocessing","Metadata extraction is limited to fields present in input file; cannot infer missing metadata","Feather file intermediate storage adds disk I/O overhead for very small datasets (<1000 images)"],"requires":["Python 3.7+","Input file in CSV, JSON, JSONL, or Parquet format","Sufficient disk space for temporary feather shards (typically 10-20% of final dataset size)"],"input_types":["CSV with URL column","JSON/JSONL with URL and metadata fields","Parquet files with URL column"],"output_types":["Sharded feather files for downstream processing","Work unit assignments for distributed workers"],"categories":["data-processing-analysis","dataset-preparation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-img2dataset__cap_1","uri":"capability://data.processing.analysis.concurrent.http.image.downloading.with.thread.pooling","name":"concurrent http image downloading with thread pooling","description":"The Downloader component creates a thread pool to fetch multiple images concurrently from URLs, integrating HTTP request handling, optional hash verification, robots.txt directive checking, image decoding, and error handling throughout the pipeline. Each worker maintains its own thread pool, allowing fine-grained control over concurrency levels and connection pooling. The architecture supports custom HTTP headers, timeout configuration, and graceful handling of network failures with retry logic.","intents":["I need to download 10 million images from URLs as fast as possible","I want to respect robots.txt directives while downloading images from websites","I need to verify image integrity using hashes during download"],"best_for":["teams building large-scale web-scraped datasets","researchers downloading public image collections","ML practitioners creating training datasets from URL lists"],"limitations":["Thread pool concurrency is limited by GIL in CPython; actual parallelism depends on I/O blocking","No built-in rate limiting per domain; aggressive downloading may trigger IP bans","robots.txt checking is advisory only; does not enforce legal compliance","Timeout configuration is global; cannot set per-domain timeouts"],"requires":["Python 3.7+","Network connectivity with sufficient bandwidth","HTTP/HTTPS access to image URLs","Optional: hash values in metadata for verification"],"input_types":["URL strings","URL with optional hash metadata","Custom HTTP headers (dict)"],"output_types":["Downloaded image bytes","Image metadata (size, format, EXIF data)","Error logs with failure reasons"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-img2dataset__cap_2","uri":"capability://image.visual.multi.mode.image.resizing.and.normalization","name":"multi-mode image resizing and normalization","description":"The Resizer component applies configurable image transformations including multiple resize modes (e.g., center crop, pad, stretch), format conversion, and quality normalization. It supports various resize strategies to handle aspect ratio preservation, enabling datasets with consistent dimensions for model training. The component integrates with the download pipeline to process images immediately after decoding, reducing memory footprint by avoiding storage of full-resolution intermediates.","intents":["I need to resize all downloaded images to 224x224 for a vision model","I want to preserve aspect ratios while creating a uniform dataset","I need to convert images to a specific format (JPEG, PNG, WebP) with quality control"],"best_for":["ML engineers preparing datasets for specific model architectures","teams standardizing image dimensions across heterogeneous sources","researchers optimizing storage by converting to efficient formats"],"limitations":["Resize modes are predefined; custom aspect ratio handling requires code modification","Quality settings are global; cannot apply per-image quality based on content","Lossy compression (JPEG) may degrade images; no adaptive quality based on image complexity","Resizing happens in-pipeline; cannot be skipped without code changes"],"requires":["Python 3.7+","PIL/Pillow or compatible image library","Target image dimensions specified in configuration"],"input_types":["Decoded image objects","Image format (JPEG, PNG, WebP, etc.)","Resize mode specification"],"output_types":["Resized image bytes","Image metadata (final dimensions, format, file size)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-img2dataset__cap_3","uri":"capability://data.processing.analysis.distributed.dataset.writing.with.multiple.output.formats","name":"distributed dataset writing with multiple output formats","description":"The SampleWriter component outputs processed images and metadata in multiple formats optimized for different ML frameworks (WebDataset, Parquet, LMDB, TFRecord). It handles sharded output to avoid bottlenecks, writing data in parallel across workers. The component manages file organization, metadata serialization, and format-specific optimizations (e.g., tar-based streaming for WebDataset, columnar storage for Parquet). This architecture enables seamless integration with downstream ML pipelines.","intents":["I need to output my dataset in WebDataset format for PyTorch training","I want to save images and metadata in Parquet for analytics and exploration","I need to create an LMDB dataset for fast random access during training"],"best_for":["ML engineers preparing datasets for specific training frameworks","teams building production ML pipelines with format-specific requirements","researchers needing multiple output formats for different experiments"],"limitations":["Output format must be chosen at pipeline start; cannot generate multiple formats in single run","Sharded output requires downstream tools to handle shard merging for some formats","Format-specific optimizations may not be optimal for all use cases","Metadata serialization format is fixed per output type; custom serialization requires extension"],"requires":["Python 3.7+","Target output format library (webdataset, pyarrow, lmdb, tensorflow, etc.)","Sufficient disk space for output dataset"],"input_types":["Processed image bytes","Image metadata (dict)","Output format specification"],"output_types":["WebDataset tar files","Parquet files with image and metadata columns","LMDB database files","TFRecord files"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-img2dataset__cap_4","uri":"capability://automation.workflow.multiprocessing.based.single.machine.distribution","name":"multiprocessing-based single-machine distribution","description":"The multiprocessing distributor allocates work units across multiple CPU cores on a single machine using Python's multiprocessing module. It spawns worker processes that each run independent Downloader instances, coordinating through a shared work queue and logger process. This strategy maximizes hardware utilization for datasets that fit within single-machine resources, avoiding the overhead of distributed computing frameworks.","intents":["I want to download a 1 million image dataset using all 16 cores on my machine","I need to process images in parallel without setting up a Spark cluster","I want to maximize CPU and network utilization on a single powerful server"],"best_for":["teams with access to high-core-count machines (16+ cores)","researchers processing datasets that fit within single-machine RAM","developers prototyping pipelines before scaling to distributed systems"],"limitations":["Limited to single machine resources; cannot scale beyond available RAM and cores","Python GIL limits true parallelism for CPU-bound operations; I/O-bound downloads benefit more","Process spawning overhead is significant for very small datasets","No fault tolerance; machine failure loses all in-progress work"],"requires":["Python 3.7+","Multi-core CPU (2+ cores recommended)","Sufficient RAM for worker processes (typically 100MB-1GB per worker)"],"input_types":["Work unit assignments from Reader","Configuration specifying number of workers"],"output_types":["Distributed work execution across processes","Aggregated logging and statistics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-img2dataset__cap_5","uri":"capability://automation.workflow.pyspark.based.distributed.dataset.processing","name":"pyspark-based distributed dataset processing","description":"The PySpark distributor scales image downloading across a Spark cluster by partitioning work units into RDDs and distributing them to Spark executors. Each executor runs a Downloader instance, with Spark handling fault tolerance, load balancing, and resource management. This strategy enables processing of massive datasets (billions of images) across commodity clusters while providing automatic recovery from node failures.","intents":["I need to download 1 billion images across a 100-node Spark cluster","I want automatic fault tolerance and recovery if cluster nodes fail","I need to leverage existing Spark infrastructure for dataset creation"],"best_for":["teams with existing Spark clusters","organizations processing multi-billion image datasets","enterprises with infrastructure for managing distributed computing"],"limitations":["Requires Spark cluster setup and maintenance; significant operational overhead","Spark serialization overhead adds latency per task; not optimal for small datasets","Debugging distributed Spark jobs is complex; error messages may be opaque","Network bandwidth between cluster and external URLs can become bottleneck","Requires Spark-compatible Python environment on all nodes"],"requires":["Apache Spark 2.4+","PySpark installed on all cluster nodes","Spark cluster with sufficient executor memory (2GB+ per executor recommended)","Network connectivity from cluster to image URLs"],"input_types":["Work unit RDD partitions","Spark configuration (executor count, memory, cores)"],"output_types":["Distributed execution across Spark executors","Fault-tolerant processing with automatic retries"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-img2dataset__cap_6","uri":"capability://automation.workflow.ray.based.cloud.distributed.dataset.processing","name":"ray-based cloud-distributed dataset processing","description":"The Ray distributor scales image downloading across Ray clusters (on-premises or cloud-based) by creating remote tasks that execute Downloader instances on Ray workers. Ray handles dynamic resource allocation, auto-scaling, and fault recovery. This strategy enables elastic scaling on cloud platforms (AWS, GCP, Azure) with minimal infrastructure management, supporting both on-demand and spot instances.","intents":["I want to download 500 million images using Ray on AWS with auto-scaling","I need to process images on cloud infrastructure without managing Spark clusters","I want to use spot instances to reduce costs while maintaining fault tolerance"],"best_for":["teams using cloud platforms (AWS, GCP, Azure) for ML infrastructure","organizations wanting elastic scaling without cluster management","researchers processing large datasets with variable resource needs"],"limitations":["Ray cluster setup requires cloud infrastructure knowledge; steeper learning curve than multiprocessing","Network egress costs on cloud platforms can be significant for billion-image datasets","Ray task scheduling overhead is higher than Spark for very large clusters (1000+ nodes)","Spot instance interruptions require careful handling; not transparent to user code","Ray ecosystem is younger; fewer production deployments than Spark"],"requires":["Ray 1.0+","Ray cluster (local, on-premises, or cloud-based)","Cloud credentials if using cloud provider (AWS, GCP, Azure)","Sufficient cloud quota for desired instance count"],"input_types":["Work unit assignments","Ray cluster configuration (worker count, instance type)"],"output_types":["Distributed execution across Ray workers","Fault-tolerant processing with automatic retries"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-img2dataset__cap_7","uri":"capability://automation.workflow.real.time.pipeline.monitoring.and.statistics.logging","name":"real-time pipeline monitoring and statistics logging","description":"The Logger component monitors the entire download pipeline in real-time, collecting statistics on download success rates, processing speed, error types, and resource utilization. It runs as a separate process to avoid blocking worker threads, aggregating metrics from all workers and writing periodic reports. The logger provides visibility into pipeline health, enabling detection of bottlenecks, network issues, or configuration problems.","intents":["I want to monitor download progress and see how many images per second we're processing","I need to identify which URLs are failing and why (timeouts, 404s, decode errors)","I want to track resource utilization (CPU, memory, network) during dataset creation"],"best_for":["teams running long-running dataset pipelines (hours to days)","operators managing production dataset creation infrastructure","researchers debugging pipeline performance issues"],"limitations":["Logging overhead adds ~5-10% to overall pipeline latency","Statistics are aggregated at intervals; real-time metrics have slight delay","No built-in alerting; requires external monitoring tools for production use","Log output format is fixed; custom metrics require code modification"],"requires":["Python 3.7+","Disk space for log files (typically 10-100MB for billion-image datasets)"],"input_types":["Statistics from worker processes","Error reports from downloaders"],"output_types":["Log files with timestamped statistics","Summary reports (success rate, speed, errors)","Real-time console output"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-img2dataset__cap_8","uri":"capability://automation.workflow.incremental.download.with.resume.and.deduplication","name":"incremental download with resume and deduplication","description":"The pipeline supports resuming interrupted downloads by tracking completed work units and skipping already-processed images. It uses metadata (URLs, hashes) to detect duplicates across runs, avoiding redundant downloads. This capability enables long-running pipelines to recover from failures without reprocessing, and supports incremental dataset growth by appending new images to existing datasets.","intents":["My download job failed after 8 hours; I want to resume from where it stopped","I'm adding new images to an existing dataset; I don't want to re-download images already processed","I want to deduplicate images across multiple download runs"],"best_for":["teams managing long-running dataset pipelines with unreliable networks","researchers incrementally building datasets over time","organizations maintaining datasets with periodic updates"],"limitations":["Resume state is stored locally; distributed systems require shared state store (not built-in)","Deduplication requires hash computation; adds ~5-10% overhead per image","No built-in distributed state management; requires external database for multi-machine resume","Resume state can become stale if pipeline configuration changes"],"requires":["Python 3.7+","Persistent storage for resume state (local filesystem or external database)","Hash values in metadata for deduplication (optional)"],"input_types":["Previous run state/checkpoint","URL list with optional hash metadata"],"output_types":["Resume checkpoint files","Deduplicated image list"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-img2dataset__cap_9","uri":"capability://safety.moderation.configurable.http.headers.and.robots.txt.compliance.checking","name":"configurable http headers and robots.txt compliance checking","description":"The downloader supports custom HTTP headers (User-Agent, Authorization, etc.) for accessing protected or restricted image sources. It integrates robots.txt checking to respect website crawling directives, parsing robots.txt files and validating URLs against allow/disallow rules before downloading. This enables ethical dataset creation while supporting authentication-protected image sources.","intents":["I need to download images from a site that requires specific User-Agent headers","I want to respect robots.txt directives while downloading from websites","I need to authenticate with API keys or tokens to access image URLs"],"best_for":["teams creating datasets from websites with robots.txt directives","researchers accessing authentication-protected image APIs","organizations prioritizing ethical web scraping practices"],"limitations":["robots.txt compliance is advisory only; does not enforce legal compliance or prevent IP bans","Custom headers are global; cannot set per-domain headers","robots.txt parsing may fail for malformed files; no fallback strategy","No rate limiting per domain; aggressive downloading may trigger IP bans despite robots.txt compliance"],"requires":["Python 3.7+","Custom headers as dict (optional)","Network access to robots.txt files on target domains"],"input_types":["Custom HTTP headers (dict)","URLs for robots.txt checking"],"output_types":["HTTP requests with custom headers","robots.txt compliance validation results"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","Input file in CSV, JSON, JSONL, or Parquet format","Sufficient disk space for temporary feather shards (typically 10-20% of final dataset size)","Network connectivity with sufficient bandwidth","HTTP/HTTPS access to image URLs","Optional: hash values in metadata for verification","PIL/Pillow or compatible image library","Target image dimensions specified in configuration","Target output format library (webdataset, pyarrow, lmdb, tensorflow, etc.)","Sufficient disk space for output dataset"],"failure_modes":["Requires input URLs to be in supported formats; custom formats need preprocessing","Metadata extraction is limited to fields present in input file; cannot infer missing metadata","Feather file intermediate storage adds disk I/O overhead for very small datasets (<1000 images)","Thread pool concurrency is limited by GIL in CPython; actual parallelism depends on I/O blocking","No built-in rate limiting per domain; aggressive downloading may trigger IP bans","robots.txt checking is advisory only; does not enforce legal compliance","Timeout configuration is global; cannot set per-domain timeouts","Resize modes are predefined; custom aspect ratio handling requires code modification","Quality settings are global; cannot apply per-image quality based on content","Lossy compression (JPEG) may degrade images; no adaptive quality based on image complexity","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"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":"2026-06-17T09:51:05.295Z","last_scraped_at":"2026-05-03T15:20:25.872Z","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=pypi-img2dataset","compare_url":"https://unfragile.ai/compare?artifact=pypi-img2dataset"}},"signature":"bWE/Y3urUiFc4LsqFEft6PdMt4lYTWyBzwIM1MmYZl+vWlK1n0tSp/TY5aOJtOI6KFQOeGz9bEIlfTal3WtuDw==","signedAt":"2026-06-22T01:20:57.446Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-img2dataset","artifact":"https://unfragile.ai/pypi-img2dataset","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-img2dataset","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"}}