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