Object detection identifies and locates objects in images. For an ID card detector, the immediate production challenge is preparing images and matching labels reliably: poor input data undermines the model, regardless of the detector used. section 7.0 section 7.3.1
A YOLOv8-ready dataset needs a predictable layout:
- Separate training, validation, and test splits, each with images and corresponding labels. section 7.3.1
- Matching filenames across the image and label directories; for example, images/id0042.png pairs with labels/id0042.txt. section 7.3.1
- A configuration file giving the dataset root, each split’s image location, and zero-indexed class names. The ID card example has one class: 0: id_card. section 7.3.1
Listing 7.9 Configuration training YOLOv8 with a custom dataset
path: /home/jovyan/data/ train: "train/images" val: "val/images" test: "test/images" names: 0: id_card
Use this configuration as a compact check of the dataset paths and single-class mapping before training. section 7.3.1
Make preparation repeatable with Kubeflow Pipelines components: download and extract the data into a dataset artifact, split paired images and labels into training, validation, and test outputs, then inspect output files as a sanity check. The illustrated split uses 75/15/10% and accepts a random-state parameter. Dataset conversion to the required YOLO format has already been done; a new dataset may require that work first. section 7.3.1
Check for image drift
After preparing the ID card data, compare image properties across datasets rather than treating images like tabular feature rows. In a test that deliberately reduces image brightness, deepchecks ImagePropertyDrift compares training and test datasets and reports drift scores for brightness, contrast, aspect ratio, area, and color intensity. A detected shift flags data worth investigating; it does not, by itself, establish that detections have failed. section 11.2.1
The prepared data feeds a broader workflow of modular YOLO training, validation, and metric tracking. Keep dataset layout, component outputs, and image-quality checks explicit so the detector can move beyond a notebook experiment into a reproducible pipeline. section 7.3.1 section 8.0
