dmlc/decord

Health v2 · maintenance only

Adoption not part of the score

2514 stars · 233 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Decord is a C++ library with Python bindings that provides efficient video and audio decoding for deep learning pipelines. It wraps hardware-accelerated decoders like FFMPEG/LibAV and Nvidia NVDEC and specializes in fast random access and shuffling of video frames during training.

Use cases

• load video frames randomly for training neural networks
• decode video datasets with random access patterns
• slice video and audio in sync for multimodal models
• build video classification or action recognition data loaders
• accelerate video decoding with GPU (NVDEC)
• extract audio tracks from video files for ML

When to choose

• you need fast random frame access from videos during deep learning training
• you want synchronized video and audio decoding in one tool
• you need hardware-accelerated decoding via NVDEC or FFmpeg backends
• you want a simple pip-installable video loader with framework bridges

When to avoid

• you only need video playback or editing rather than ML data loading
• you need actively developed features or recent codec support
• you require GPU decoding via prebuilt pip wheels (only CPU wheels are published)
• you need a full video processing/transcoding toolkit

Facets

library · maturity maintenance

video-processing audio-processing machine-learning data-science machine-learning deep-learning computer-vision media windows python cpp video-loader video-decoding random-access ffmpeg nvidia-codecs data-loading pytorch tensorflow video linux macos gpu

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