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Rename Frontend to ModelFormat in documents (#265)
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@@ -33,7 +33,7 @@ python infer.py --model yolov7.onnx --image 000000014439.jpg --device gpu --use_
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## YOLOv7 Python接口
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```python
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fastdeploy.vision.detection.YOLOv7(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX)
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fastdeploy.vision.detection.YOLOv7(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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```
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YOLOv7模型加载和初始化,其中model_file为导出的ONNX模型格式
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@@ -43,7 +43,7 @@ YOLOv7模型加载和初始化,其中model_file为导出的ONNX模型格式
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> * **model_file**(str): 模型文件路径
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> * **params_file**(str): 参数文件路径,当模型格式为ONNX格式时,此参数无需设定
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> * **runtime_option**(RuntimeOption): 后端推理配置,默认为None,即采用默认配置
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> * **model_format**(Frontend): 模型格式,默认为ONNX
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> * **model_format**(ModelFormat): 模型格式,默认为ONNX
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### predict函数
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@@ -34,7 +34,7 @@ The visualisation of the results is as follows.
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## YOLOv7 Python Interface
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```python
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fastdeploy.vision.detection.YOLOv7(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX)
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fastdeploy.vision.detection.YOLOv7(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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```
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YOLOv7 model loading and initialisation, with model_file being the exported ONNX model format.
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@@ -44,7 +44,7 @@ YOLOv7 model loading and initialisation, with model_file being the exported ONNX
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> * **model_file**(str): Model file path
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> * **params_file**(str): Parameter file path. If the model format is ONNX, the parameter can be filled with an empty string.
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> * **runtime_option**(RuntimeOption): Back-end inference configuration. The default is None, i.e. the default is applied
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> * **model_format**(Frontend): Model format. The default is ONNX format
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> * **model_format**(ModelFormat): Model format. The default is ONNX format
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### Predict Function
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