Model Training - Imagery Analysis

Feature Description

Model training aims to use generated training data for neural network model training. The complete training procedure obtains optimized network models through multiple iterations (epochs). Hyperparameter tuning (learning rate, batch size, etc.) can enhance training efficiency and accuracy to derive usable neural network models.
Additionally, fine-tuning pretrained models with limited sample data can better adapt models to specific application scenarios while reducing training time.

Parameter Description

Parameter Default Description Type
Training data path   Specifies the path of the generated training data String
Model purpose Object detection Specifies the application purpose of the training data Object
Model algorithm   Select appropriate model algorithm based on application purpose String
Training config file   Specifies the path of the training configuration file String
Training times 10 Number of training epochs. Default: 10. Increased epochs improve model fitting but may cause overfitting. Maximum allowed: 300 epochs. Training duration is proportional to epoch count. Integer
Batch size 1 Number of images processed per training step. Higher batch sizes increase memory consumption but reduce training time within reasonable limits. Integer
Learning rate   Parameter update magnitude. Leave blank for automatic adjustment based on batch size and built-in baseline settings. Double
Training log path   Specifies the save path for training logs String
Load pretrained model   Enable to add pretrained model path for transfer learning Boolean
Pretrained model   Specifies pretrained model file (*.sdm) for transfer learning String
Fine-tuning method Full fine-tuning Segformer and Mask2Former support full/LoRA fine-tuning. Other models only support full fine-tuning. LoRA enables efficient transfer learning for large-parameter models. String
Processor type GPU Specifies processor type String
GPU index 0 Input GPU index(es). For multi-GPU training, use comma-separated values (e.g., "0,1,2"). String
Model save path   Specifies model storage path String
Model name saved_model Specifies model name String

Output

Generated model files and training logs.