VART Post-Processing Support - VART Post-Processing Support - 6.3 English - XD056

Vitis Video Analytics SDK (VVAS) 6.3 User Guide (XD056)

Document_ID
XD056
Release_Date
2026-09-24
Version
6.3 English

VVAS historically supports post-processing based on the VvasCore post-processing library interface, where users can create custom post-processing libraries using defined interfaces to hook into the vvas_xinfer plugin flow without modifying the plugin code. VVAS provides example post-processing libraries for ResNet50 and YOLO models as demonstration use cases and reference implementations.

Traditional VvasCore Post-Processing:

"postprocess-config": {
    "library-path": "/usr/lib/libvvascore_postprocess_resnet50-1.0.so",
    "library-config": {
        "topk": 1,
        "label-file-path": "/etc/vvas/configs/infer/imagenet-classes-1000.txt"
    }
}

VART-Based Post-Processing:

With this release, VVAS also supports VART-based post-processing operations that are based on operators rather than model types. This provides flexibility to compose post-processing pipelines using various operators. VVAS includes a standalone library implementation (libvvascore_postprocess_vart.so) for the VART-X interface, requiring no modifications to the infer plugin itself.

Supported VART Post-Processing Operators:

Classification:

  • SOFTMAX

  • ARGMAX

  • TOPK

  • THRESHOLD

  • LABEL_MAPPING

  • NORMALIZATION

  • CALIBRATION_TEMPERATURE

  • CALIBRATION_PLATT

  • BIAS_CORRECTION

  • OUTLIER_DETECTION

  • UNCERTAINTY_ESTIMATION

Detection:

  • NMS

  • ANCHOR_ADJUSTMENT

  • CLASSWISE_NMS

  • DISTANCE_IOU_NMS

  • OBJECT_COUNT

  • SOFT_NMS

Segmentation:

  • SOFTMAXSEG

  • SIGMOIDSEG

  • ARGMAXSEG

Example VART Post-Processing Configuration:

The following is an example post-processing configuration using the SOFTMAX operator for ResNet50:

"postprocess-config": {
    "library-path": "/usr/lib/libvvascore_postprocess_vart.so",
    "library-config": {
        "topk": 1,
        "label-file-path": "/etc/vvas/configs/infer/imagenet-classes-1000.txt",
        "type": "SOFTMAX",
        "post-process-print": "false",
        "model-params": {
            "quant-scale-factors": [4.0]
        }
    }
}

Note

When using VART-based post-processing, ensure that the models are compiled with the flags in vitisai_config.json “fe_experiment”: “edge-quantization-in-rt=1 enable-output-without-rt-support=1”.

For detailed information about VART post-processing operators and their configuration, refer to the VART documentation.