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The directory structure of examples is as follows:
.
β”œβ”€β”€ caffe
β”‚   β”œβ”€β”€ mobilenet_v2                    # mobilenet_v2 float model
β”‚   └── vgg-ssd                         # vgg-ssd float model
β”œβ”€β”€ onnx
β”‚   β”œβ”€β”€ resnet50v2                      # resnet50v2 float model
β”‚   └── yolov5                          # yolov5 float model
β”œβ”€β”€ pytorch
β”‚   β”œβ”€β”€ resnet18                        # resnet18 float model
β”‚   β”œβ”€β”€ resnet18_qat                    # resnet18 QAT model
β”‚   β”œβ”€β”€ resnet18_export_onnx            # how to export onnx model from pytorch
β”‚   └── yolov5                          # yolov5 float model
β”œβ”€β”€ tensorflow
β”‚   β”œβ”€β”€ ssd_mobilenet_v1                # ssd_mobilenet_v1 float model
β”‚   └── inception_v3_qat                # inception_v3 QAT model
β”œβ”€β”€ tflite
β”‚   β”œβ”€β”€ mobilenet_v1                    # mobilenet_v1 float model
β”‚   └── mobilenet_v1_qat                # mobilenet_v1 QAT model
β”œβ”€β”€ darknet
β”‚   └── yolov3_416x416                  # yolov3 float model
└── functions
    β”œβ”€β”€ accuracy_analysis               # how to use accuracy-analysis function
    β”œβ”€β”€ codegen                         # how to generate c++ deployment demo when converting model
    β”œβ”€β”€ custom_op                       # How to use custom_op function
    β”œβ”€β”€ dynamic_shape                   # how to use dynamic shape function
    β”œβ”€β”€ hybrid_quant                    # how to use hybrid-quantization function
    β”œβ”€β”€ model_pruning                   # how to use model_pruning function
    β”œβ”€β”€ multi_batch                     # how to expand batch for use multi-batch function
    β”œβ”€β”€ multi_input                     # How to convert multi-input model
    β”œβ”€β”€ npu_device_test                 # how to test npu device by connect the board
    β”œβ”€β”€ onnx_edit                       # how to use onnx_edit function
    └── quantize_algorithm_mmse         # how to use MMSE quantize algorithm