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