The inference model (the model saved by `paddle.jit.save`) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment.
The model saved during the training process is the checkpoints model, which saves the parameters of the model and is mostly used to resume training.
Compared with the checkpoints model, the inference model will additionally save the structural information of the model. Therefore, it is easier to deploy because the model structure and model parameters are already solidified in the inference model file, and is suitable for integration with actual systems.
Next, we first introduce how to convert a trained model into an inference model, and then we will introduce mainbody detection, feature extraction based on inference model,
then we introduce a recognition pipeline consist of mainbody detection, feature extraction and vector search. At last, we introduce classification base on inference model.
-[CONVERT TRAINING MODEL TO INFERENCE MODEL](#CONVERT)
-[Convert feature extraction model to inference model](#Convert_feature_extraction)
-[Convert classification model to inference model](#Convert_class)
-[MAINBODY DETECTION MODEL INFERENCE](#DETECTION_MODEL_INFERENCE)
-[FEATURE EXTRACTION MODEL INFERENCE](#FEATURE_EXTRACTION_MODEL_INFERENCE)
-[CONCATENATION OF MAINBODY DETECTION, FEATURE EXTRACTION AND VECTOR SEARCH](#CONCATENATION)
-[CLASSIFICATION MODEL INFERENCE](#CLASSIFICATION)
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## CONVERT TRAINING MODEL TO INFERENCE MODEL
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### Convert feature extraction model to inference model
First please enter the root folder of PaddleClas. Download the product feature extraction model:
The above model is trained on AliProduct with ResNet50_vd as backbone. To convert the trained model into an inference model, just run the following command:
```
# -c Set the training algorithm yml configuration file
# -o Set optional parameters
# Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
# Global.save_inference_dir Set the address where the converted model will be saved.
When converting to an inference model, the configuration file used is the same as the configuration file used during training. In addition, you also need to set the `Global.pretrained_model` parameter in the configuration file.
After the conversion is successful, there are three files in the model save directory:
```
├── product_ResNet50_vd_aliproduct_v1.0_infer
│ ├── inference.pdiparams
│ ├── inference.pdiparams.info
│ └── inference.pdmodel
```
<aname="Convert_class"></a>
### Convert classification model to inference model
