Quantization and Deployment Tips for YOLO - 2.0 English

Vitis AI Optimizer User Guide (UG1333)

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2.0 English
After converting to the Caffe model, YOLOv3 can be deployed to the FPGA after quantization. Follow Vitis AI User Guide to quantize and deploy.
  1. Set preprocessing for YOLO in prototxt.

    YOLO has its own preprocessing scheme. This must be specified for better performance. Set yolo_height and yolo_width to the input height and width of network in transform_param as shown in the following code snippet.

    layer {
      name: "data"
      type: "ImageData"
      top: "data"
      top: "label"
      include {
        phase: TEST
      transform_param {
        mirror: false
        yolo_height: 768
        yolo_width: 1280
      image_data_param {
        source: "file_list.txt"
        root_folder: "/data/dataset/"
        batch_size: 10
        shuffle: false
  2. Provide labels in file list.

    Though labels are not required for quantization and will not affect the results, the ImageData layer in Caffe check labels in the source file. Ensure the file list assigned in source has a number after each image file name. Example of source file list is shown as follows:

    ILSVRC2012_val_00000001.JPEG 65
    ILSVRC2012_val_00000002.JPEG 970
    ILSVRC2012_val_00000003.JPEG 230
    ILSVRC2012_val_00000004.JPEG 809
    ILSVRC2012_val_00000005.JPEG 516