Link to this sectionCOCO128-Seg Dataset#

Link to this sectionIntroduction#

Ultralytics COCO128-Seg is a small but versatile instance segmentation dataset composed of the first 128 images of the COCO train 2017 set. This dataset is ideal for testing and debugging segmentation models, or for experimenting with new detection approaches. With 128 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.

Link to this sectionDataset Structure#

• Images: 128 total, with train and val split identically (see note below).
• Classes: Same 80 object categories as COCO.
• Labels: YOLO-format polygons stored in labels/train2017 for the shared train and val image directory.
• Download size: ~7 MB.

This dataset is intended for use with Ultralytics Platform and YOLO26.

Link to this sectionDataset YAML#

A YAML file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO128-Seg dataset, the coco128-seg.yaml file is maintained at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco128-seg.yaml.

# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

# COCO128-seg dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
# Documentation: https://docs.ultralytics.com/datasets/segment/coco128-seg
# Example usage: yolo train data=coco128-seg.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── coco128-seg ← downloads here (7 MB)

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: coco128-seg # dataset root dir
train: images/train2017 # train images (relative to 'path') 128 images
val: images/train2017 # val images (relative to 'path') 128 images
test: # test images (optional)

# Classes
names:
  0: person
  1: bicycle
  2: car
  3: motorcycle
  4: airplane
  5: bus
  6: train
  7: truck
  8: boat
  9: traffic light
  10: fire hydrant
  11: stop sign
  12: parking meter
  13: bench
  14: bird
  15: cat
  16: dog
  17: horse
  18: sheep
  19: cow
  20: elephant
  21: bear
  22: zebra
  23: giraffe
  24: backpack
  25: umbrella
  26: handbag
  27: tie
  28: suitcase
  29: frisbee
  30: skis
  31: snowboard
  32: sports ball
  33: kite
  34: baseball bat
  35: baseball glove
  36: skateboard
  37: surfboard
  38: tennis racket
  39: bottle
  40: wine glass
  41: cup
  42: fork
  43: knife
  44: spoon
  45: bowl
  46: banana
  47: apple
  48: sandwich
  49: orange
  50: broccoli
  51: carrot
  52: hot dog
  53: pizza
  54: donut
  55: cake
  56: chair
  57: couch
  58: potted plant
  59: bed
  60: dining table
  61: toilet
  62: tv
  63: laptop
  64: mouse
  65: remote
  66: keyboard
  67: cell phone
  68: microwave
  69: oven
  70: toaster
  71: sink
  72: refrigerator
  73: book
  74: clock
  75: vase
  76: scissors
  77: teddy bear
  78: hair drier
  79: toothbrush

# Download script/URL (optional)
download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128-seg.zip

Link to this sectionUsage#

To train a YOLO26n-seg model on the COCO128-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model Training page.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo26n-seg.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="coco128-seg.yaml", epochs=100, imgsz=640)

Link to this sectionSample Images and Annotations#

Here are some examples of images from the COCO128-Seg dataset, along with their corresponding annotations:

• Mosaiced Image: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.

Link to this sectionCitations and Acknowledgments#

If you use the COCO dataset in your research or development work, please cite the following paper:

@misc{lin2015microsoft,
      title={Microsoft COCO: Common Objects in Context},
      author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
      year={2015},
      eprint={1405.0312},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the COCO dataset website.

Link to this sectionFAQ#

Link to this sectionWhat is the COCO128-Seg dataset, and how is it used in Ultralytics YOLO26?#

The COCO128-Seg dataset is a compact instance segmentation dataset by Ultralytics, consisting of the first 128 images from the COCO train 2017 set. This dataset is tailored for testing and debugging segmentation models or experimenting with new detection methods. It is particularly useful with Ultralytics YOLO26 and Platform for rapid iteration and pipeline error-checking before scaling to larger datasets. For detailed usage, refer to the model Training page.

Link to this sectionHow can I train a YOLO26n-seg model using the COCO128-Seg dataset?#

To train a YOLO26n-seg model on the COCO128-Seg dataset for 100 epochs with an image size of 640, you can use Python or CLI commands. Here's a quick example:

from ultralytics import YOLO

# Load a model
model = YOLO("yolo26n-seg.pt")  # Load a pretrained model (recommended for training)

# Train the model
results = model.train(data="coco128-seg.yaml", epochs=100, imgsz=640)

For a thorough explanation of available arguments and configuration options, you can check the Training documentation.

Link to this sectionWhy is the COCO128-Seg dataset important for model development and debugging?#

Because the download and train/val loop are much smaller than full COCO, COCO128-Seg lets you run a 1-epoch sanity check on a new pipeline — verifying the model trains, validates, and saves checkpoints correctly — before scaling to the full COCO-Seg dataset. Learn more about supported dataset formats in the Ultralytics segmentation dataset guide.

Link to this sectionWhere can I find the YAML configuration file for the COCO128-Seg dataset?#

The YAML configuration file for the COCO128-Seg dataset is available in the Ultralytics repository. You can access the file directly at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco128-seg.yaml. The YAML file includes essential information about dataset paths, classes, and configuration settings required for model training and validation.

Link to this sectionHow does COCO128-Seg compare to COCO8-Seg and the full COCO-Seg dataset?#

COCO128-Seg (128 images) sits between COCO8-Seg (8 images) and the full COCO-Seg dataset (118,287 training images) in terms of size:

• COCO8-Seg: 8 images (4 train, 4 val) — ideal for quick sanity checks and debugging.
• COCO128-Seg: 128 images — balanced between size and diversity, with train and val sharing the same directory.
• Full COCO-Seg: 118,287 training images — comprehensive but resource-intensive, requiring ~27 GB on first download.

COCO128-Seg offers more diversity than COCO8-Seg while remaining far more manageable than the full COCO-Seg dataset for experimentation and initial model development.