Loading Images from Tar Archives

Load and process JPEG images from tar archives using NeMo Curator’s DALI-powered ImageReaderStage.

The ImageReaderStage uses NVIDIA DALI for high-performance image decoding with GPU acceleration and automatic CPU fallback, designed for processing large collections of images stored in tar files.

How it Works

The ImageReaderStage processes directories containing .tar files with JPEG images. While tar files may contain other file types (text, JSON, etc.), the stage extracts only JPEG images for processing.

Directory Structure Example

dataset/
├── 00000.tar
│   ├── 000000000.jpg
│   ├── 000000001.jpg
│   ├── 000000002.jpg
│   ├── ...
├── 00001.tar
│   ├── 000001000.jpg
│   ├── 000001001.jpg
│   ├── ...

What gets processed:

• JPEG images: All .jpg files within tar archives

What gets ignored:

• Text files (.txt), JSON files (.json), and other non-JPEG content within tar archives
• Any files outside the tar archives (like standalone Parquet files)

Usage

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from nemo_curator.pipeline import Pipeline
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from nemo_curator.stages.file_partitioning import FilePartitioningStage
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from nemo_curator.stages.image.io.image_reader import ImageReaderStage
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# Create pipeline
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pipeline = Pipeline(name="image_loading", description="Load images from tar archives")
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# Stage 1: Partition tar files for parallel processing
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pipeline.add_stage(FilePartitioningStage(
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    file_paths="/path/to/tar_dataset",
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    files_per_partition=1,
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    file_extensions=[".tar"],
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))
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# Stage 2: Read JPEG images from tar files using DALI
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pipeline.add_stage(ImageReaderStage(
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    dali_batch_size=100,
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    verbose=True,
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    num_threads=8,
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    num_gpus_per_worker=0.25,
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))
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# Run the pipeline
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results = pipeline.run()

Parameters:

• file_paths: Path to directory containing tar files
• files_per_partition: Number of tar files to process per partition (controls parallelism)
• dali_batch_size: Number of images per ImageBatch for processing

ImageReaderStage Details

The ImageReaderStage is the core component that handles tar archive loading with the following capabilities:

DALI Integration

• Automatic Device Selection: Uses GPU decoding when CUDA is available, CPU decoding otherwise
• Tar Archive Reader: Leverages DALI’s tar archive reader to process tar files
• Batch Processing: Processes images in configurable batch sizes for memory efficiency
• JPEG-Only Processing: Extracts only JPEG files (ext=["jpg"]) from tar archives

Image Processing

• Format Support: Reads only JPEG images (.jpg) from tar files
• Size Preservation: Maintains original image dimensions (no automatic resizing)
• RGB Output: Converts images to RGB format for consistent downstream processing
• Metadata Extraction: Creates ImageObject instances with image paths and generated IDs

Error Handling

• Missing Components: Skips missing or corrupted images with missing_component_behavior="skip"
• Graceful Fallback: Automatically falls back to CPU processing if GPU is unavailable
• Validation: Validates tar file paths and provides clear error messages
• Non-JPEG Filtering: Silently ignores non-JPEG files within tar archives

Parameters

ImageReaderStage Parameters

Output Format

The pipeline produces ImageBatch objects containing ImageObject instances for downstream curation tasks. Each ImageObject contains:

• image_data: Raw image pixel data as numpy array (H, W, C) in RGB format
• image_path: Path to the original image file in the tar
• image_id: Unique identifier extracted from the filename
• metadata: Additional metadata dictionary

Example ImageObject structure:

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ImageObject(
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    image_path="00000.tar/000000031.jpg",
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    image_id="000000031", 
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    image_data=np.array(...),  # Shape: (H, W, 3)
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    metadata={}
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)

Note: Only JPEG images are extracted from tar files. Other content (text files, JSON metadata, etc.) within the tar archives is ignored during processing.