2 مستودعات
Decodes and samples video frames using GPU acceleration for deep learning data pipelines.
Distinct from Video Frame Processing: Distinct from Video Frame Processing: focuses on GPU-accelerated decoding and frame sampling for data pipelines, not general frame adjustments.
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NVIDIA DALI is a GPU-accelerated data loading and preprocessing library designed for deep learning workflows. It constructs high-performance data pipelines that offload decoding, augmentation, and normalization to the GPU, eliminating CPU bottlenecks in training and inference. The library reads data from multiple storage formats and streams it directly into GPU memory, with support for multi-GPU execution to scale throughput across large-scale workloads. DALI distinguishes itself by enabling data pipelines to be built once and executed across multiple deep learning frameworks without code cha
Decodes and samples video frames on the GPU for deep learning data preprocessing pipelines.
VidGear is a high-performance Python video processing framework designed for capturing, transcoding, and manipulating video streams. It functions as a multi-protocol video streamer and a WebRTC streaming server, enabling the transfer of video frames over networks using RTSP, RTMP, RTP, and MJPEG protocols. The project distinguishes itself through hardware-accelerated video transcoding and decoding using GPU backends like CUDA to reduce CPU load. It includes a cross-platform screen capture tool and a specialized system for establishing direct peer-to-peer media connections using WebRTC signali
Offloads pixel processing and decompression to CUDA or CUVID backends to reduce CPU utilization.