@@ -153,13 +153,7 @@ result = clas.predict(infer_imgs)
print(next(result))
```
**Note**: The result returned by model.predict() is a `generator`, so you need to use the `next()` function to call it or `for loop` to loop it. And it will predict with batch_size size batch and return the prediction results when called. The default batch_size is 1, and you also specify the batch_size when instantiating, such as model = paddleclas.PaddleClas(model_name="PPHGNet_small", batch_size=2). The result of demo above:
**Note**: The result returned by model.predict() is a `generator`, so you need to use the `next()` function to call it or `for loop` to loop it. And it will predict with batch_size size batch and return the prediction results when called. The default batch_size is 1, and you also specify the batch_size when instantiating, such as `model = paddleclas.PaddleClas(model_name="PPHGNet_small", batch_size=2)`.
<aname="3"></a>
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@@ -212,7 +206,7 @@ where `train/` and `val/` are the training set and validation set, respectively.
#### 3.3.1 Train ImageNet
The PPHGNet_small training configuration is provided in `ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml`, which can be started with the following script:
The PPHGNet_small training configuration is provided in `ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml`, which can be started with the following script:
* The current model with the best accuracy will be saved in `output/PPHGNet_small/best_model.pdparams`
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#### 3.3.2 Fine-tuning based on ImageNet weights
If you are not training an ImageNet task, you need to change the configuration file and training method, such as reducing the learning rate, reducing the number of epochs, etc.
If you are not training an ImageNet task, you need to change the configuration file and training method, such as reducing the learning rate, reducing the number of epochs, etc.
<aname="3.4"></a>
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@@ -265,13 +259,13 @@ The results:
**Note**:
* Among the above command, argument `-o Global.pretrained_model="output/PPLCNet_x1_0/best_model"` specify the path of the best model weight file. You can specify other path if needed.
* Among the above command, argument `-o Global.pretrained_model="output/PPHGNet_small/best_model"` specify the path of the best model weight file. You can specify other path if needed.
* The default test image is `docs/images/inference_deployment/whl_demo.jpg` ，And you can test other image, only need to specify the argument `-o Infer.infer_imgs=path_to_test_image`.
* The default output is the value of Top-5. If you want to output the value of Top-k, you can specify `-o Infer.PostProcess.topk=k`, where `k` is the value you specify.
* The default label mapping is based on the ImageNet dataset. If you change the dataset, you need to re-specify `Infer.PostProcess.class_id_map_file`. For the method of making the mapping file, please refer to `ppcls/utils/imagenet1k_label_list.txt`
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@@ -410,4 +404,3 @@ PaddleClas provides an example of how to deploy on mobile by Paddle-Lite. Please
Paddle2ONNX support convert Paddle Inference model to ONNX model. And you can deploy with ONNX model on different inference engine, such as TensorRT, OpenVINO, MNN/TNN, NCNN and so on. About Paddle2ONNX details, please refer to [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX).
PaddleClas provides an example of how to convert Paddle Inference model to ONNX model by paddle2onnx toolkit and predict by ONNX model. You can refer to [paddle2onnx](../../../deploy/paddle2onnx/readme_en.md) for deployment details.
