Datasets:

DasKunststoffZentrumSKZ
/

Errors_Additive_Manufacturing_Plattform_Cam

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Errors_Additive_Manufacturing_Plattform_Cam

3D Printing Nozzle Camera – YOLO Object Detection Dataset

This Repository is part of the Project: Künstliche Intelligenz zur Automatiserten Fehlerkorrektur in der Additiven Fertigung(Förderkennzeichen: 16IS23050B).

This dataset contains images captured from a camera positioned to capture the whole plattform of a 3D printer. The task is object detection of both regular print elements and typical printing defects.

The dataset is provided in standard YOLO format and can be used directly with Ultralytics YOLO.

Content

The plattform camera captures:

Nozzle
Printed Object
Purge Line
Typical 3D printing defects
Spaghetti

Classes

Nozzle
Object
Purge Line
Spaghetti
Stringing
Unterextrusion
Warping
schlechte erste Schicht
Double Print

Number of classes: 9

Structure


Errors_Additive_Manufacturing_Plattform_Cam/
│
├── images/
│   ├── train/
│   ├── val/
│   └── test/
│
├── labels/
│   ├── train/
│   ├── val/
│   └── test/
│
├── dataset.yaml
├── summary_classes.csv
└── summary_splits.csv

YOLO Label Format

Each image has a corresponding .txt annotation file.

Format per row


<class_id> <x_center> <y_center> <width> <height>

Details

Coordinates are normalized to [0,1]
class_id corresponds to the class index defined in dataset.yaml
Multiple objects are stored as multiple rows in one file

Example


0 0.512 0.423 0.120 0.085
3 0.621 0.558 0.210 0.175

Training with Ultralytics YOLO

Example training script:

from ultralytics import YOLO

model = YOLO("yolov8n.pt")  # or your pretrained checkpoint

data_path = "dataset/dataset.yaml"

results = model.train(
    data=data_path,
    epochs=100,
    imgsz=640,
    patience=15,
    device="cuda",
    seed=42,
    save=True,
    name="model_finetuned"
)

Installation

pip install ultralytics

Example dataset.yaml

path: Errors_Additive_Manufacturing_Plattform_Cam
train: images/train
val: images/val
test: images/test

names:
  0: error_type_1
  1: error_type_2
  2: error_type_3
  3: error_type_4

Statistics

Included summary files:

summary_splits.csv (images and boxes per split)
summary_classes.csv (class distribution)

License

Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0)

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