Custom Training

Overview

Welcome to this step-by-step tutorial on building model training applications with Supervisely SDK! This guide will show you how to set up a training app with a built-in user interface and all the necessary tools to seamlessly manage the training process. Supervisely SDK has a dedicated TrainApp class that takes care of the heavy lifting, so you can focus directly on your model and training logic without worrying about the underlying infrastructure.

We'll use the Train RT-DETRv2 example to walk you through the process.

What the TrainApp class offers:

• Built-in GUI: Simple and easy-to-follow customizable interface.

• Train and Val Splits: Handles splitting of your project into training and validation sets.

• Data Preparation: Easily convert Supervisely annotation format into one of the popular formats with a single line of code (e.g., COCO annotation format).

• Project Versioning: Saves project versions to reproduce training with the same data and annotations.

• Model Evaluation: Model Evaluation Benchmark automatically runs evaluation of your model and generate a detailed report with metrics and visualizations.

• Model Export: You can add exporting of your model to ONNX or TensorRT format.

• Model Saving: Automatically saves your model and related files to Supervisely Team Files.

Step-by-Step Implementation

Let's dive into the steps required to integrate your custom model using the TrainApp class.

1. Prepare Model Configurations: Create a models.json file with a list of model configurations.

2. Prepare Hyperparameters: Define default hyperparameters and save to a .yaml file.

3. Prepare App Options: Add optional features to control the GUI layout and behavior.

4. The TrainApp: Initialize the TrainApp class with the required parameters.

5. Integrate Your Custom Model: Implement your custom model training logic using the TrainApp wrapper.

6. Enhancements: Enhance your training app with additional features like a progress bar or model evaluation.

7. Run the Application: Launch the training app locally and deploy it to the Supervisely platform.

1. Prepare Model Configurations

To enable selection of a model configuration for training, create a models.json file. This JSON file consists of a list of dictionaries, each detailing a specific model configuration. The information from this file will populate a table in your app's GUI, allowing users to select a model.

You can also add information or URLs of pretrained checkpoints and weights to enable fine-tuning of existing models.

📄 See source file for the RT-DETRv2 models.json

Example models.json

[
    {
        "Model": "RT-DETRv2-S",
        "dataset": "COCO",
        "AP_val": 48.1,
        "Params(M)": 20,
        "FPS(T4)": 217,
        "meta": {
            "task_type": "object detection",
            "model_name": "RT-DETRv2-S",
            "model_files": {
                "checkpoint": "https://github.com/lyuwenyu/storage/releases/download/v0.2/rtdetrv2_r18vd_120e_coco_rerun_48.1.pth",
                "config": "rtdetrv2_r18vd_120e_coco.yml"
            }
        }
    },
    // ... additional models
]

Example GUI preview:

Model Selection

Table Fields

Each dictionary item in models.json represents a single model as a row, with all its fields, except for the meta field, acting as columns. You can customize these fields to display the necessary information.

Technical Field (meta)

Each model configuration must have a meta field. This field is not displayed in the table but contains essential information required in training to properly build the model.

Here are the required fields:

• (required) task_type: A computer vision task type (e.g., object detection).

• (required) model_name: Model configuration name.

• (required) model_files: A dict with files needed to load the model, such as model weights, config file. You can extend it with additional files if needed.

  • (optional) checkpoint: Path or URL to the model weights to enable fine-tuning.

  • (optional) config: Path to the model configuration file.

  • (optional) Any additional files can be added to the model_files dictionary that are required for your model.

2. Prepare Hyperparameters

Define your default hyperparameters save to a .yaml file (e.g., hyperparameters.yaml). Path to this file is then passed to the TrainApp for training configuration.

You can access hyperparameters later in the code by using train.hyperparameters in your training logic.

📄 See source file for the RT-DETRv2 hyperparameters.yaml

Example hyperparameters.yaml

epoches: 50
batch_size: 16
eval_spatial_size: [640, 640]  # height, width

checkpoint_freq: 5
save_optimizer: false
save_ema: false

optimizer:
  type: AdamW
  lr: 0.0001
  betas: [0.9, 0.999]
  weight_decay: 0.0001

clip_max_norm: 0.1

lr_scheduler:
  type: MultiStepLR  # CosineAnnealingLR | OneCycleLR
  milestones: [35, 45]  # epochs
  gamma: 0.1

lr_warmup_scheduler:
  type: LinearWarmup
  warmup_duration: 1000  # steps

use_ema: True 
ema:
  type: ModelEMA
  decay: 0.9999
  warmups: 2000

use_amp: True

3. Prepare App Options

You can provide additional options to control the GUI layout and behavior. Create an app_options.yaml file to enable or disable features.

📄 See source file for the RT-DETRv2 app_options.yaml

Example app_options.yaml

device_selector: false
model_benchmark: true
show_logs_in_gui: true
collapsable: false
auto_convert_classes: true

Available options

Click to expand

• device_selector

  • description: Whether to add a selector widget into the GUI for choosing the CUDA device.

  • default: false

  • options: true, false

• model_benchmark

  • description: Whether to allow user to run model benchmark after training. If true, checkboxes and parameters for benchmarking will be displayed in the GUI.

  • default: false

  • options: true, false

• export_onnx_supported

  • description: If true, adds a checkbox allowing to export model to ONNX format. Requires implementing of Exporting to ONNX.
  • default: false

  • options: true, false

• export_tensorrt_supported

  • description: If true, adds a checkbox allowing to export model to TensorRT format. Requires implementing of Exporting to TensorRT.
  • default: false

  • options: true, false

• train_logger

  • description: If set to tensorboard, the TensorBoard server will start automatically for displaying training logs, such as losses, metrics, etc. If None, you need to start the server manually.

  • default: None

  • options: None, tensorboard

• show_logs_in_gui

  • description: Show logs window in GUI during training

  • default: true

  • options: true, false

• auto_convert_classes

  • description: Automatically converts classes according to the model CV task for model benchmark and creates a new project.

  • default: false

  • options: true, false

• collapsable | Beta feature

  • description: Collapse GUI cards after confirming the current step and moving to the next one.

  • default: false

  • options: true, false

• demo | For developers

  • description: Path to the demo folder containing tutorial data on how to use the model outside of the Supervisely platform

  • default: None

  • options: path/to/demo/folder

4. TrainApp Class

Now that you have prepared the necessary files, you can initialize the TrainApp class with the required parameters. The TrainApp class is the core component that manages the training process and provides a user-friendly interface for interacting with the training app.

TrainApp Signature

class TrainApp(
    framework_name: str,
    models: Union[str, List[Dict[str, Any]]],
    hyperparameters: str,
    app_options: Union[str, Dict[str, Any]] = None,
    work_dir: str = None,
)

Parameters	Type	Description
framework_name	str	Name of the ML framework used for training
models	Union[str, List[dict]]	Path to models.json file with model configurations, or a list of the same JSON
hyperparameters	str	Path to hyperparameters.yaml file
app_options	Optional[str]	Path to app_options.yaml file
work_dir	Optional[str]	Local path for storing intermediate files, such as downloaded model files

Important TrainApp Attributes

• train.work_dir - Path to the working directory. Contains intermediate files, such as downloaded model files.

• train.output_dir - Path to the output directory. Contains training results, logs, and checkpoints.

Project-related Attributes

• train.project_id - Supervisely project ID

• train.project_name - Project name

• train.project_info - Contains project information (ProjectInfo object)

• train.project_meta - ProjectMeta object with classes/tags info
• train.project_dir - Project directory path

• train.train_dataset_dir - Training dataset directory path

• train.val_dataset_dir - Validation dataset directory path

• train.sly_project - Supervisely sly.Project object

Model-related Attributes

• train.model_name - Name of the selected model

• train.model_source - Indicates if the model is pretrained or custom (trained in Supervisely)

• train.model_files - Dictionary containing paths to model files (e.g., checkpoint and optional config)

• train.model_info - Entry from the models.json for the selected model if model is selected from the list of pretrained models, otherwise experiment info dict.
• train.hyperparameters - Dictionary of selected hyperparameters

• train.classes - List of selected class names

• train.device - Selected CUDA device

5. Integrate Your Custom Model

Now it's time to integrate your custom model using the TrainApp class. We'll use the RT-DETRv2 model as an example. You can always refer to the source code for Train RT-DETRv2 on GitHub.

5.1 Initialize Your Imports

import os
import sys
import yaml

# Add path to the source code of PyTorch model architecture
sys.path.insert(0, "rtdetrv2_pytorch")

import supervisely as sly
from rtdetrv2_pytorch.src.core import YAMLConfig
from rtdetrv2_pytorch.src.solver import DetSolver
from supervisely.nn import ModelSource, RuntimeType
from supervisely.nn.training.train_app import TrainApp

from supervisely_integration.serve.rtdetrv2 import RTDETRv2

5.2 Initialize the TrainApp

Create an instance of the TrainApp by providing the framework name, model configuration file, hyperparameters file, and app options file.

base_path = "supervisely_integration/train"
train = TrainApp(
    framework_name="RT-DETRv2",
    models=f"supervisely_integration/models_v2.json",
    hyperparameters=f"{base_path}/hyperparameters.yaml",
    app_options=f"{base_path}/app_options.yaml",
)

5.3 Prepare Training data

The TrainApp gives you access to the Supervisely Project containing the train and val datasets. Convert these datasets to the desired format (e.g., COCO) and run your training routine. You can use built-in converters like to_coco to convert your datasets to the required format or write your custom converter if required.

Use train.sly_project to access the Supervisely project in the training code.

Data Conversion Example:

def convert_data() -> Tuple[str, str]:
    # Get selected project from TrainApp
    project = train.sly_project
    meta = project.meta

    # Convert train dataset to COCO format
    train_dataset: sly.Dataset = project.datasets.get("train")
    train_ann_path = train_dataset.to_coco(meta, dest_dir=train_dataset.directory)

    # Convert val dataset to COCO format
    val_dataset: sly.Dataset = project.datasets.get("val")
    val_ann_path = val_dataset.to_coco(meta, dest_dir=val_dataset.directory)
    return train_ann_path, val_ann_path

5.4 Implement Training Routine

All training code should be implemented in the function under the @train.start decorator and should return the experiment information dictionary.

Returned dictionary should contain the following fields:

• model_name - Name of the model used for training.

• model_files - Dictionary with paths to additional model files (e.g. config). These files together with checkpoints will be uploaded to Supervisely Team Files automatically.

• checkpoints - A list of checkpoint paths, or an output directory with checkpoints. These checkpoints will be uploaded to Supervisely Team Files automatically.

• best_checkpoint - Name of the best checkpoint file.

These fields will be validated upon training completion, and if one of them is missing, the training will be considered as failed.

In this example training logic and loop are inside solver.fit() function.

📄 See source code for the RT-DETRv2 main.py and training logic

Training Routine with @train.start Decorator

@train.start
def start_training():
    # 1️⃣ Convert data to required format
    train_ann_path, val_ann_path = convert_data()

    # 2️⃣ Get the path to the selected checkpoint
    checkpoint = train.model_files["checkpoint"]

    # 3️⃣ Prepare custom config for training
    custom_config_path = prepare_config(train_ann_path, val_ann_path)
    cfg = YAMLConfig(
        custom_config_path,
        tuning=checkpoint,
    )
    output_dir = cfg.output_dir
    os.makedirs(output_dir, exist_ok=True)
    model_config_path = f"{output_dir}/model_config.yml"
    with open(model_config_path, "w") as f:
        yaml.dump(cfg.yaml_cfg, f)
    remove_include(model_config_path)

    # 4️⃣ Start tensorboard logs
    tensorboard_logs = f"{output_dir}/summary"
    train.start_tensorboard(tensorboard_logs)
    
    # 5️⃣ Start training
    solver = DetSolver(cfg)
    solver.fit()

    # 6️⃣ Return experiment information
    experiment_info = {
        "model_name": train.model_name,
        "model_files": {"config": model_config_path},
        "checkpoints": output_dir,
        "best_checkpoint": "best.pth",
    }
    return experiment_info

6. Enhancements

You can enhance your training application by adding additional features like a progress bar or model evaluation. These features provide valuable feedback to the user and help in monitoring the training process.

6.1 Progress Bar with the Train Logger ⏱️

Enhance your training feedback by incorporating a progress bar via the Supervisely train_logger.

Simply import train_logger from supervisely.nn.training and use it in your training loop.

from supervisely.nn.training import train_logger

train_logger.train_started(total_epochs)
for epoch in range(start_epoch, total_epochs):
    train_logger.epoch_started(total_steps)
    ...
    train_logger.epoch_finished()
train_logger.train_finished()

6.2 Register the Inference Class for model evaluation 📊

If you plan to use Evaluation Model Benchmark, you need to implement Inference class and register it for TrainApp. Refer to the Integrate Custom Inference guide for more information.

train.register_inference_class(RTDETRv2)

7. Run the Application

Now that you've integrated your custom model, you're ready to launch the training application. You can choose to run it locally for testing or deploy it directly to the Supervisely platform. The training app functions like any other Supervisely app, but with a built-in GUI.

Useful links:

• Environment variables - Setting up environment variables for Supervisely App development
• App developemnt - Basics of Supervisely App development
• Hello world! - Simple example of a Supervisely App

Run and debug locally

Prepare environment file before running the app:

# Change values to your own

PROJECT_ID=12208 # ⬅️ change it
APP_NAME=Train RT-DETRv2 # ⬅️ change it
DEBUG_APP_DIR=app_data # ⬅️ change it

You can run and debug your training app locally using the following shell command:

uvicorn main:train.app --host 0.0.0.0 --port 8000 --ws websockets

After running the app, you can access it at http://localhost:8000.

If you are a VSCode user, you can create .vscode/launch.json configuration to run and debug your training app locally.

Example launch.json

Click to expand

{
  "version": "0.2.0",
  "configurations": [
    {
      "name": "Uvicorn Train",
      "type": "debugpy",
      "request": "launch",
      "module": "uvicorn",
      "args": [
        "main:train.app",
        "--host",
        "0.0.0.0",
        "--port",
        "8000",
        "--ws",
        "websockets",
      ],
      "justMyCode": false,
      "env": {
        "PYTHONPATH": "${workspaceFolder}:${PYTHONPATH}",
        "LOG_LEVEL": "DEBUG",
        "DEBUG_APP_DIR": "app_data"
      }
    }
    ]
}

Deploy to Supervisely

Releasing your private app to the Supervisely platform is very simple. You will need a git repository with your app code, Supervisely SDK and credentials to access the platform. You can refer to Add private app for more information.

Follow steps below to release your app:

1. [Optional] git clone your app code if you haven't done it yet:

   git clone "your_app_repo_url"
   cd "your_app_repo"
2. [Optional] create a python virtual environment:

   python3 -m venv .venv
   source .venv/bin/activate
3. Install Supervisely SDK:

   pip install supervisely
4. Provide your Supervisely credentials in one of the following ways:

   • Create a file supervisely.env in the home directory with the fields:

     SERVER_ADDRESS=https://app.supervisely.com
     API_TOKEN=your_personal_token
   • Set environment variables:

     export SERVER_ADDRESS=https://app.supervisely.com
     export API_TOKEN=your_personal_token
5. Run CLI command to deploy the app:

   supervisely release

   If the app code is in a subdirectory, you can specify the path to the app code (directory with config.json file):

   supervisely release --sub-app "path/to/app"

Final Steps 📦

After training is completed successfully, the TrainApp will automatically prepare and upload the model and all related files to Supervisely storage. During this finalization phase, TrainApp executes several important steps to ensure your experiment's outputs are correctly validated, processed, and stored.

Standard Supervisely storage path for the artifacts: /experiments/{project_id}_{project_name}/{task_id}_{framework_name}/

Here's what happens:

1. Validating of experiment_info The system checks the experiment_info dictionary to ensure all required fields (like model name, file paths, etc.) are correct and complete. This step is essential to prevent any missing or incorrect metadata.

2. Postprocess Training Artifacts Additional processing is applied to the raw training outputs (e.g., cleaning up temporary files, formatting logs, and aggregating metrics) to generate a standardized set of artifacts.

3. Postprocess Training and Validation Splits The splits for training and validation data are further refined if needed. This step ensures consistency and prepares the splits for future reference or re-training if necessary.

4. Upload Model Files and Checkpoints to Supervisely Storage All model files provided in experiment info and checkpoints (e.g., best, intermediate, and last checkpoints) are automatically uploaded to the Supervisely storage so that they are safely stored and can be accessed later.

5. Create and Upload model_meta.json A metadata file (model_meta.json) is generated, which includes essential details about the model (such as its architecture, training parameters, and version). This file is then uploaded along with other artifacts.

6. Run Model Benchmarking (if enabled) If model benchmarking is enabled in your app_options, the system will run automated evaluation and generate a detailed report with metrics and visualizations. This report is then uploaded to the Supervisely platform.

7. Export Model to ONNX and TensorRT (if enabled) For ease of deployment, the model may be automatically exported to additional formats like ONNX and TensorRT. This ensures compatibility with different serving environments.

8. Generate and Upload Additional Training Files Other supplementary files such as logs, experiment reports, and configuration files are packaged and uploaded. These additional artifacts help in future debugging, auditing, or model evaluation.

Training Artifacts

The final training artifacts are organized and stored in the Supervisely storage directory. This directory contains all the necessary files and metadata related to the training experiment.

Output artifacts directory structure:

📦265_RT-DETRv2
 ┣ 📂checkpoints
 ┃ ┣ 📜best.pth
 ┃ ┣ 📜checkpoint0025.pth
 ┃ ┣ 📜checkpoint0050.pth
 ┃ ┗ 📜last.pth
 ┣ 📂export
 ┃ ┗ 📜best.onnx
 ┣ 📂logs
 ┃ ┗ 📜events.out.tfevents.1738673672.sly-app-265-lghhx.101.0
 ┣ 📜Model Evaluation Report.lnk
 ┣ 📜app_state.json
 ┣ 📜experiment_info.json
 ┣ 📜hyperparameters.yaml
 ┣ 📜model_config.yml
 ┣ 📜model_meta.json
 ┣ 📜open_app.lnk
 ┗ 📜train_val_split.json

Experiment Info

Experiment info contains all the necessary information about the training experiment. This file is generated automatically by the TrainApp and is used to store metadata, paths to model files, checkpoints, and other artifacts, making it easy to track and manage the training process.

This file is essential and used by the Supervisely platform to organize and display models in a structured manner in training and serving apps.

Example experiment_info.json

This is an example of the final experiment information file that is generated:

{
  "experiment_name": "265_Lemons_RT-DETRv2-M",
  "framework_name": "RT-DETRv2",
  "model_name": "RT-DETRv2-M",
  "task_type": "object detection",
  "project_id": 27,
  "task_id": 265,
  "model_files": {
    "config": "model_config.yml"
  },
  "checkpoints": [
    "checkpoints/best.pth",
    "checkpoints/checkpoint0025.pth",
    "checkpoints/checkpoint0050.pth",
    "checkpoints/last.pth"
  ],
  "best_checkpoint": "best.pth",
  // If export to ONNX is enabled and implemented
  "export": {
    "ONNXRuntime": "export/best.onnx"
  },
  // ----------------------------
  "app_state": "app_state.json",
  "model_meta": "model_meta.json",
  "train_val_split": "train_val_split.json",
  "train_size": 4,
  "val_size": 2,
  "hyperparameters": "hyperparameters.yaml",
  "artifacts_dir": "/experiments/27_Lemons/265_RT-DETRv2/",
  "datetime": "2025-02-04 12:56:57",
  // If model benchmarking is enabled and implemented
  "evaluation_report_id": 21037,
  "evaluation_report_link": "https://app.supervisely.com/model-benchmark?id=21037",
  "evaluation_metrics": {
    "mAP": 1,
    "AP50": 1,
    "AP75": 1,
    "f1": 0.8333333333333333,
    "precision": 1,
    "recall": 0.75,
    "iou": 0.9704312784799517,
    "classification_accuracy": 1,
    "calibration_score": 0.8725475046690554,
    "f1_optimal_conf": 0.5143424272537231,
    "expected_calibration_error": 0.12745249533094466,
    "maximum_calibration_error": 0.35718443564006264
  },
  // -----------------------------------
  "logs": {
    "type": "tensorboard",
    "link": "/experiments/27_Lemons/265_RT-DETRv2/logs/"
  }
}

Fields explanation:

All paths listed in the experiment_info.json are relative to the artifacts_dir field.

• experiment_name, framework_name, model_name, task_type: General metadata about the training experiment.

• project_id, task_id: IDs used to uniquely identify the project and the training task within Supervisely.

• model_files: Contains paths to the model configuration file(s).

• checkpoints & best_checkpoint: Lists the checkpoints produced during training and indicates the best-performing one.

• export: Shows the export file for ONNX (or TensorRT if enabled).

• app_state: Location of the app_state.json file for debugging and re-runs.

• model_meta: Path to the model meta file. Contains ProjectMeta object in .json format.
• train_val_split: Location of the train_val_split.json file containing the split information.

• hyperparameters: Path to the hyperparameters file used for training.

• artifacts_dir & datetime: Directory where artifacts are stored and the timestamp of the experiment.

• evaluation_report and evaluation_metrics: Information from the model benchmarking process (if run).

• logs: Location and type of training logs for review in the Supervisely interface.

Additional Resources 📚

Export Model to ONNX and TensorRT

If you'd like to export your trained model to the ONNX or TensorRT format, you can easily do so using dedicated decorators. Simply add the @train.export_onnx and @train.export_tensorrt decorators to your export functions. These functions should return the file path of the exported model, and the TrainApp will take care of uploading it to Supervisely storage automatically.

@train.export_onnx
def export_onnx_model():
    # Your export model to ONNX format
    return path_to_onnx_model

@train.export_tensorrt
def export_tensorrt_model():
    # Your export model to TensorRT format code
    return path_to_tensorrt_model

About GUI Layout

The graphical user interface (GUI) for the training app is a pre-built template based on Supervisely Widgets. Each step is organized into a separate Card that groups related settings together, making it intuitive to navigate through the training settings.

Step 1. Project Options

In this initial step, the app displays the project on which the training is run. It also offers an option to cache the project on the training agent for future use.

Step 1

Step 2. Train and Val Splits

This step allows you to split your project data into training and validation sets. You can choose among different splitting methods such as random, tags, or by datasets.

Step 2

Step 3. Classes Selector

Select the classes you want your model to train on. You can choose multiple classes from the provided classes table.

Step 3

Step 4. Model Selector

Here, you can choose the model you wish to train. Select from pretrained models or your own custom models (trained previously in Supervisely). Once trained, your custom model will automatically appear in the custom models table next time you run the app.

Step 4

Step 5. Hyperparameters

Set and adjust the training hyperparameters in this step. You can also enable model benchmarking (if enabled and implemented) and export your model to ONNX or TensorRT formats (if enabled and implemented). The hyperparameters are fully customizable using an Editor widget, allowing you to add as many variables and values as needed.

Step 5

Step 6. Training Process

Enter your experiment name, choose the CUDA device (if enabled), and start the training process. Once training begins, previous steps will be locked to prevent changes during the run.

Step 6

Step 7. Training Logs

View real-time training logs and progress via a progress bar (if implemented). This step also provides a link to the TensorBoard dashboard for more detailed monitoring (if implemented).

Step 7

Step 8. Training Artifacts

After training finishes, this step displays the final training artifacts along with links to the stored files. These artifacts are automatically uploaded and organized in the Supervisely storage.

Default artifacts location is: /experiments/{project_id}_{project_name}/{task_id}_{framework_name}/

Step 8

How to see TensorBoard logs after the training is finished

Once the training process is complete, you can view the TensorBoard logs in the logs directory within the artifacts folder.

The default location for these logs is: /experiments/{project_id}_{project_name}/{task_id}_{framework_name}/logs/

To open and view the log file, you can use Tensorboard Experiments Viewer app.

tensorboard dashboard

Debugging with app_state.json 🐞

The app_state.json file captures the state of the TrainApp right before training begins. This file is incredibly useful for debugging and troubleshooting issues that might arise during training. It allows developers to quickly restart the training process without having to reconfigure all the settings via the GUI.

You can access app_state in the training code by using train.app_state and dump it locally for debugging purposes.

@train.start
def start_training():
    ...
    app_state = train.app_state
    with open("app_state.json", "w") as f:
        json.dump(app_state, f, indent=2)
    ...

Why Use app_state.json?

• Quick Debugging: If something goes wrong during training, you can inspect app_state.json file to see the exact configuration and quickle re-run the app without manually reselecting settings in the GUI every time.

• State Preservation: It preserves the state of the training app in a human-readable .json format.

How to use app_state.json

Call load_from_app_state method to load the app state from the app_state.json file after the app is initialized.

base_path = "supervisely_integration/train"
train = TrainApp(
    framework_name="RT-DETRv2",
    models=f"supervisely_integration/models_v2.json",
    hyperparameters=f"{base_path}/hyperparameters.yaml",
    app_options=f"{base_path}/app_options.yaml",
)

train.register_inference_class(RTDETRv2)

# Call `train.gui.load_from_app_state` to load the app state
app_state = {...}
train.gui.load_from_app_state(app_state)

@train.start
def start_training():
    ...

Example of app_state.json:

{
  "input": {
    "project_id": 27
  },
  "train_val_split": {
    "method": "random",
    "split": "train",
    "percent": 80
  },
  "classes": ["kiwi", "lemon"],
  "model": {
    "source": "Pretrained models",
    "model_name": "RT-DETRv2-M"
  },
  "hyperparameters": "epoches: 20\nbatch_size: 16\neval_spatial_size: [640, 640]...",
  "options": {
    "model_benchmark": {
      "enable": true,
      "speed_test": false
    },
    "cache_project": true
  }
}

Fields in app_state.json

input

• description: Contains the input data for the training app.

• fields:

  • project_id – The ID of the project to use for training.

train_val_split

• description: Configures how the dataset is split into training and validation sets.

• fields:

  • method: The splitting method (e.g., random, tags, or datasets).

  • If method is random:

    • split: Specifies which split to use (e.g., train or val).

    • percent: The percentage of the dataset to include for training.

  • If method is tags:

    • train_tag: The tag used for the training split.

    • val_tag: The tag used for the validation split.

    • untagged_action: Action for untagged images (options: train, val, or ignore).

  • If method is datasets:

    • train_datasets: IDs of datasets used for training.

    • val_datasets: IDs of datasets used for validation.

classes

• description: List of the classes to be used during training. Must match the class names in the project.

model

• description: Specifies the model configuration for training.

• fields:

  • source: The model source, such as Pretrained models or Custom model.

  • If source is Pretrained models:

    • model_name: The name of the pretrained model (should match an entry in models.json).

  • If source is Custom model:

    • task_id: The training session ID containing the custom checkpoint.

    • checkpoint: The name of the custom checkpoint to be used.

hyperparameters

• description: Contains the hyperparameters (in .yaml format) used for training.

options

• description: Provides additional optional configurations for the training app.

• fields:

  • model_benchmark: Configuration for model benchmarking.

    • enable: Boolean to enable or disable benchmarking.

    • speed_test: Boolean to enable or disable a speed test.

  • cache_project: Boolean to enable or disable caching of the project.

  • export: Configurations for model export.

    • ONNXRuntime: Option for enabling/disabling ONNX model export.

    • TensorRT: Option for enabling/disabling TensorRT model export.