Training Model Through CVAT

Once you have annotated enough data, you can train a model to pre-annotate rest of your images. This can save a lot of time in annotation.

1 - Annotate enough images in your CVAT task.
2 - Go back to your CVAT dashboard and click on Create New Annotation Model in that task. You will see a popup with few options.
3 - Select appropriate model type (TensorFlow OD API recommended) and then select the model (i.e ssd-mobilenet-v2-coco-201).
4 - Select the machine type. A machine with multiple GPUs will speed up your training process.
5 - Enter optional arguments. See below for more details.

Arguments (optional)

You can optionally specify some arguments in the Arguments field separated by ;.

Here is a sample: epochs=100;batch_size=24.

• epochs : number of epochs to train your model for. By default, we will train for appropriate number of epochs depending upon the model.
• batch_size : batch size for the training
• initial_learning_rate : initial learning rate for the model. We recommend you do not change this.
• num_clones (default=1): number of gpus to train model on

If you select a Machine type with 4 GPUs (Tesla V100), the following command can be used: epochs=300000;num_clones=4;batch_24;

• Note that num_clones is 4 because there are 4 GPUs available.

Adding your own base model to CVAT

You can also add your own base models to CVAT via Onepanel. Please email us at info@onepanel.io to learn how.