First, please click[here](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)to download and unzip to get the test demo images.
First, please click[here](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)to download and unzip to get the test demo images.
* Prediction with CLI
...
...
@@ -124,7 +124,7 @@ result = model.predict(input_data="pulc_demo_imgs/code_exists/contains_code_demo
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="person_exists", 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="codeexists", batch_size=2)`. The result of demo above:
```
>>> result
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@@ -202,6 +202,7 @@ The datas under `code_exists` directory:
```
Where `train/` and `val/` are training set and validation set respectively. The `train_list.txt` and `val_list.txt` are label files of training data and validation data respectively.
**Note：**
* About the contents format of `train_list.txt` and `val_list.txt`, please refer to [Description about Classification Dataset in PaddleClas](../data_preparation/classification_dataset_en.md).
