Handling your Dataset

Before you start training your model, you need to load and organize your image data. A clean and well-labeled dataset is the foundation for accurate AI performance.

1. Load Your Images

Access the Dataset tab in your ONE AI workspace to prepare your visual training data.

• You can simply drag and drop your image files to import them.
• You can Import Files or Import Folders to load unlabeled image data directly from your device.
• The Import Dataset feature can be used to load a labeled dataset.
• You can use the Camera Tool in the AI tab to capture images directly within ONE AI for real-time data collection.
• You can use the Spectrogram Generator to convert audio or time series data to images.

1.1 Dataset Import

When you press the Import Existing Dataset button, the following window opens:

You need to select the directory to import as well as the format of your annotations. Currently, we support the formats YOLO, COCO and Pascal Voc as well as Classification datasets that contain one directory for each class.

Check out our examples like if you don't have a dataset and want to try ONE AI:

1. A quality control of potato chips
2. A detection of handwritten digits
3. An object detection of dice

1.2 Camera Tool

You can use OneWare Studio's integrated Camera Tool to record images for your dataset. The camera tool allows you to configure camera settings and record images from multiple cameras simultaneously.

When you start the camera tool, you first need to add the cameras to your workspace. Select the camera that you want to use in the dropdown menu and click on the plus icon. You can use the play icon on the bottom of the camera window to start and stop the live preview. By clicking on the gear icon, you can enter the camera configuration menu.

The camera configuration menu allows you to customize your camera settings. You can create different presets that can be exported and imported. You are also able to crop the image. You can draw the area you are interested in onto the preview or set its coordinates at the bottom of the settings list.
For our example, we increased the the camera's brightness and contrast. This brightens the white background and makes it easier for the AI model to recognize the numbers. We also added a crop to select a square region from the rectangular camera image.

To record images, we need to go to the Capture tab. You can record images by clicking on the camera icon, which updates the preview. This records an image from all cameras simultaneously. You need to select the directory where you want to add them and click on the save icon to save them. The images are automatically named with the timestamp they were captured at, so you don't need to enter names manually.

1.3 Spectrogram Generator

The spectrogram generator supports converting audio or CSV files to spectrogram images. You can decide, whether the generated spectrograms are added to the train, test or validation dataset. Furthermore, you need to provide the sampling rate of your data.

2. Choose Labeling Mode

• Classes: Each image is assigned to one or more classes.
• Objects: Individual objects are selected in the images by drawing boxes around them.

3. Divide Your Dataset

Dividing your data into separate subsets is crucial for building reliable AI models. Follow these steps to split your data effectively.

3.1 Training Set

The training set teaches your AI what to recognize - it's your model's foundation.
Use about 70% of your total dataset with properly labeled images. Ideally it should include at least 50 images per class. More variety means better real-world performance.

3.2 Validation Set

The validation set monitors your model's performance on unseen data during training.
This evaluates performance without direct training involvement. Labels are required for the validation set as well to monitor the AI performance on unseen data while training.

Using Validation Split: If you don't have separate validation images, you can enable Use Validation Split to auto-divide your training set:

• 20% for standard datasets
• 30% for small datasets
• 10% for large datasets

3.3 Test Set

The test set provides a final performance evaluation after training. It is important to keep this set completely separate from training and validation data to get an objective evaluation. Providing labels is optional but highly recommended. Otherwise, you need to manually look through the predictions instead of getting a quantitative result.
To get an accurate evaluation on how the model will perform for your application, it is important to ensure that the test data represents the real-world deployment conditions. This organized approach ensures your AI model will be robust, accurate, and ready for real-world deployment with ONE AI.

If you don't have a separate test dataset, you can use the images from the train or validation dataset to test your AI. Because ONE AI only uses the validation dataset to stop the training when there is no more improvement and not for hyperparameter settings, the results should not be too far off, if you use the validation dataset for the final evaluation.

4 Add Your Labels

Open the Labels tab and create labels for each class you want to detect, e.g. "defect" or "strawberry". You can assign unique colors to make the annotation process faster and easier.

4.1 The two Label Modes

• Classes: Assign classes to entire images.
  Example: "defective" or "non-defective" for quality control

  

• Objects: Mark specific objects by drawing bounding boxes. It is possible to annotate multiple objects and labels per image.
  Example: individual boxes for "Strawberry" or "Foreign Object"