COCO8-Multispectral Dataset

Introduction

The Ultralytics COCO8-Multispectral dataset is an advanced variant of the original COCO8 dataset, designed to facilitate experimentation with multispectral object detection models. It consists of the same 8 images from the COCO train 2017 set—4 for training and 4 for validation—but with each image transformed into a 10-channel multispectral format. By expanding beyond standard RGB channels, COCO8-Multispectral enables the development and evaluation of models that can leverage richer spectral information.

COCO8-Multispectral is fully compatible with Ultralytics HUB and YOLO11, ensuring seamless integration into your computer vision workflows.

Dataset Generation

The multispectral images in COCO8-Multispectral were created by interpolating the original RGB images across 10 evenly spaced spectral channels within the visible spectrum. The process includes:

• Wavelength Assignment: Assigning nominal wavelengths to the RGB channels—Red: 650 nm, Green: 510 nm, Blue: 475 nm.
• Interpolation: Using linear interpolation to estimate pixel values at intermediate wavelengths between 450 nm and 700 nm, resulting in 10 spectral channels.
• Extrapolation: Applying extrapolation with SciPy's interp1d function to estimate values beyond the original RGB wavelengths, ensuring a complete spectral representation.

This approach simulates a multispectral imaging process, providing a more diverse set of data for model training and evaluation. For further reading on multispectral imaging, see the Multispectral Imaging Wikipedia article.

Dataset YAML

The COCO8-Multispectral dataset is configured using a YAML file, which defines dataset paths, class names, and essential metadata. You can review the official coco8-multispectral.yaml file in the Ultralytics GitHub repository.

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

# COCO8-Multispectral dataset (COCO8 images interpolated across 10 channels in the visual spectrum) by Ultralytics
# Documentation: https://docs.ultralytics.com/datasets/detect/coco8-multispectral/
# Example usage: yolo train data=coco8-multispectral.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── coco8-multispectral  ← downloads here (20.2 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: ../datasets/coco8-multispectral # dataset root dir
train: images/train # train images (relative to 'path') 4 images
val: images/val # val images (relative to 'path') 4 images
test: # test images (optional)

# Number of multispectral image channels
channels: 10

# 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/coco8-multispectral.zip

import cv2
import numpy as np

# Create and write 10-channel TIFF
image = np.ones((10, 640, 640), dtype=np.uint8)  # CHW-order
cv2.imwritemulti("example.tiff", image)

# Read TIFF
success, frames_list = cv2.imreadmulti("example.tiff")
image = np.stack(frames_list, axis=2)
print(image.shape)  # (640, 640, 10)  HWC-order for training and inference

Usage

To train a YOLO11n model on the COCO8-Multispectral dataset for 100 epochs with an image size of 640, use the following examples. For a comprehensive list of training options, refer to the YOLO Training documentation.

from ultralytics import YOLO

# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")

# Train the model on COCO8-Multispectral
results = model.train(data="coco8-multispectral.yaml", epochs=100, imgsz=640)

# Train YOLO11n on COCO8-Multispectral using the command line
yolo detect train data=coco8-multispectral.yaml model=yolo11n.pt epochs=100 imgsz=640

For more details on model selection and best practices, explore the Ultralytics YOLO model documentation and the YOLO Model Training Tips guide.

Sample Images and Annotations

Below is an example of a mosaiced training batch from the COCO8-Multispectral dataset:

• Mosaiced Image: This image demonstrates a training batch where multiple dataset images are combined using mosaic augmentation. Mosaic augmentation increases the diversity of objects and scenes within each batch, helping the model generalize better to various object sizes, aspect ratios, and backgrounds.

This technique is especially valuable for small datasets like COCO8-Multispectral, as it maximizes the utility of each image during training.

Citations and Acknowledgments

If you use the COCO dataset in your research or development, 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}
}

Special thanks to the COCO Consortium for their ongoing contributions to the computer vision community.

FAQ

What Is the Ultralytics COCO8-Multispectral Dataset Used For?

The Ultralytics COCO8-Multispectral dataset is designed for rapid testing and debugging of multispectral object detection models. With only 8 images (4 for training, 4 for validation), it is ideal for verifying your YOLO training pipelines and ensuring everything works as expected before scaling to larger datasets. For more datasets to experiment with, visit the Ultralytics Datasets Catalog.

How Does Multispectral Data Improve Object Detection?

Multispectral data provides additional spectral information beyond standard RGB, enabling models to distinguish objects based on subtle differences in reflectance across wavelengths. This can enhance detection accuracy, especially in challenging scenarios. Learn more about multispectral imaging and its applications in advanced computer vision.

Is COCO8-Multispectral Compatible With Ultralytics HUB and YOLO Models?

Yes, COCO8-Multispectral is fully compatible with Ultralytics HUB and all YOLO models, including the latest YOLO11. This allows you to easily integrate the dataset into your training and validation workflows.

Where Can I Find More Information on Data Augmentation Techniques?

For a deeper understanding of data augmentation methods such as mosaic and their impact on model performance, refer to the YOLO Data Augmentation Guide and the Ultralytics Blog on Data Augmentation.

Can I Use COCO8-Multispectral for Benchmarking or Educational Purposes?

Absolutely! The small size and multispectral nature of COCO8-Multispectral make it ideal for benchmarking, educational demonstrations, and prototyping new model architectures. For more benchmarking datasets, see the Ultralytics Benchmark Dataset Collection.

📅 Created 29 days ago ✏️ Updated 25 days ago

Comments