Instructions for use
The purpose of Model Training is to conduct neural network Model Training using the generated training data, and iteratively evaluate the model according to Set Parameters to obtain the available neural network model.
SuperMap has built-in open source frameworks such as TensorFlow and Pytorch, and trains Machine Learning or Deep Learning models with different Dataset categories. The overall Training Procedure obtains the network model with better Training Result through multiple epochs, and uses the pre-training model based on large-scale basic training data in the training model to reduce the training time. Improve Model Training efficiency and accuracy through hyperparameter tuning (learning rate, Batchsize, etc.). The desktop-side training tool is suitable for short-cycle Model Training.
Parameter Description
| Parameter name | Default | Parameter description | Parameter Type |
|---|---|---|---|
| Train the data path | Specify the generated training data path | String | |
| Purpose of the training model | Object Detection | A purpose of that train data is specified, Provide Object Detection, Binary Classification, Multiple Classification, Scene Classification, Object Extraction, Detect Common Change six uses. | Object |
| Model Algorithm | Customize | Specify the Model Algorithm | String |
| Train the Config File | Specify the training configuration and ask for that path. | String | |
| Training Times | 10 | Number of times all training data participated in Model Training | Integer |
| Amount of single step operation | 1 | The number of pictures of single step operation in one training | Integer |
| Learning rate | Magnitude of update of model parameters | Double | |
| Training log path | Specify the Save path for the training low | String | |
| Load Pretrained Model (Optional) |
false | Check to add a pre-trained model path | Boolean |
| Processor type | GPU | Specifies the processor type | String |
| GPU number | 0 | Enter the GPU number | String |
| Model storage path | Specify the Result Data storage path | String | |
| Model name | saved_model | Specify the generation data Folder Name | String |
Output Result
Data folder generated by Model Training.