Create a model
Create a customized training model to automate your project and speed up the annotation process all while delivering quality results. Only Team Owners and Team Admins can run model trainings.
Step 1: Enter your model’s name and description
- Go to Neural Network.
- Click the + button.
- Type the model’s name and description.
Step 2: Choose the annotation task
- Click Annotation Task.
- Choose an annotation task from the drop-down list.
For Image (Legacy) Projects:
- Instance segmentation
- Semantic segmentation
For Image Projects:
- Instance segmentation
- Object detection
Step 3: Choose the base model
- Click Base Model.
- Choose the base model upon which your new model will be refined from the drop-down list.
Step 4: Choose the training data
- Click Train Data.
- Choose the projects, folders, or root images that you want to use to train your model from the drop-down list. Only projects and folders that contain completed images appear in the drop-down list.
Step 5: Choose the testing data
- Click Test Data.
- Choose the projects, folders, or images that you want to use to evaluate your model from the drop-down list. Only projects and folders that contain completed images appear in the drop-down list.
Step 6: Set your training’s parameters
|Base model||The base model upon which your new model will be refined.|
|Number of devices (GPUs)||The details of the devices (GPUs) available to train your model. You have two options: a 12GB GPU or a 16GB GPU.|
|Epoch count||The number of times the dataset undergoes training. The epoch count ranges from 1 to 200.|
|Batch size||The number of images.|
|Learning rate||The model’s learning speed. The learning rate ranges from 0 to 1.|
|Image (ROIs) per batch||The number of regions of interest (ROI) per image. The ROI ranges from 2 to 512 and is a power of 2. The smaller the ROI, the faster the learning rate. Image (ROIs) per batch works only with instance segmentation.|
|Evaluation period||The number of epochs per evaluation period. If the epoch count is 10 and the evaluation period is 2, it means that the dataset will undergo an evaluation every 2 epochs. The user will be reported of the best model that resulted from the evaluation period.|
|Gamma||Gamma is needed for the learning rate. Its value ranges from 0 to 1, and it will be multiplied with the learning rate.|
|Epochs for gamma||After you specify the number of epochs for gamma, the learning rate will be multiplied by gamma.|
To run the training:
- In the top right corner, click Run training.
- In the pop-up message, click Run Training.
After the training is complete, you can use your new model to run Smart Prediction.
To run Smart Prediction with a specific model:
raw_images = sa.search_items( project = "Cityscapes", annotation_status = "NotStarted") succeeded, failed = sa.run_prediction( project = "Cityscapes", images_list = raw_images, model = "Cityscapes Segmentation Model")
To stop the training:
- Click Stop in the top right corner.
- In the pop-up message, click Yes.
Get model training metrics
To download your training's metrics:
- Go to your model.
- Under Training metrics, click Download.
To download the trained model and its metadata:
sa.download_model( model = "Cityscapes Segmentation Model", output_dir = "./models")
The downloaded model directory contains trained model weights and a configuration file on the model architecture.
Model deployment tutorials
Check out our GitHub repository for model deployment tutorials on edge devices like Jetson Nano and OAK-D.
Updated 5 months ago