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# COCO-Seg Dataset

The COCO-Seg dataset, an extension of the COCO (Common Objects in Context) dataset, is specially designed to aid research in object instance segmentation. It uses the same images as COCO but introduces more detailed segmentation annotations. This dataset is a crucial resource for researchers and developers working on instance segmentation tasks, especially for training Ultralytics YOLO models.

## COCO-Seg Pretrained Models

Model | size (pixels) | mAPbox 50-95 | mAPmask 50-95 | Speed CPU ONNX (ms) | Speed T4 TensorRT10 (ms) | params (M) | FLOPs (B)
--- | --- | --- | --- | --- | --- | --- | ---
YOLO11n-seg | 640 | 38.9 | 32.0 | 65.9 ± 1.1 | 1.8 ± 0.0 | 2.9 | 10.4
YOLO11s-seg | 640 | 46.6 | 37.8 | 117.6 ± 4.9 | 2.9 ± 0.0 | 10.1 | 35.5
YOLO11m-seg | 640 | 51.5 | 41.5 | 281.6 ± 1.2 | 6.3 ± 0.1 | 22.4 | 123.3
YOLO11l-seg | 640 | 53.4 | 42.9 | 344.2 ± 3.2 | 7.8 ± 0.2 | 27.6 | 142.2
YOLO11x-seg | 640 | 54.7 | 43.8 | 664.5 ± 3.2 | 15.8 ± 0.7 | 62.1 | 319.0

## Key Features

- COCO-Seg retains the original 330K images from COCO.
- The dataset consists of the same 80 object categories found in the original COCO dataset.
- Annotations now include more detailed instance segmentation masks for each object in the images.
- COCO-Seg provides standardized evaluation metrics like mean Average Precision (mAP) for object detection, and mean Average Recall (mAR) for instance segmentation tasks, enabling effective comparison of model performance.

## Dataset Structure

The COCO-Seg dataset is partitioned into three subsets:

1. Train2017: This subset contains 118K images for training instance segmentation models.
2. Val2017: This subset includes 5K images used for validation purposes during model training.
3. Test2017: This subset encompasses 20K images used for testing and benchmarking the trained models. Ground truth annotations for this subset are not publicly available, and the results are submitted to the COCO evaluation server for performance evaluation.

## Applications

COCO-Seg is widely used for training and evaluating deep learning models in instance segmentation, such as the YOLO models. The large number of annotated images, the diversity of object categories, and the standardized evaluation metrics make it an indispensable resource for computer vision researchers and practitioners.

## Dataset YAML

A YAML (Yet Another Markup Language) 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 COCO-Seg dataset, the `coco.yaml` file is maintained at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml.

ultralytics/cfg/datasets/coco.yaml

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

    # COCO 2017 dataset https://cocodataset.org by Microsoft
    # Documentation: https://docs.ultralytics.com/datasets/detect/coco/
    # Example usage: yolo train data=coco.yaml
    # parent
    # ├── ultralytics
    # └── datasets
    #     └── coco ← downloads here (20.1 GB)

    # 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: coco # dataset root dir
    train: train2017.txt # train images (relative to 'path') 118287 images
    val: val2017.txt # val images (relative to 'path') 5000 images
    test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794

    # 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: |
      from pathlib import Path

      from ultralytics.utils.downloads import download

      # Download labels
      segments = True  # segment or box labels
      dir = Path(yaml["path"])  # dataset root dir
      url = "https://github.com/ultralytics/assets/releases/download/v0.0.0/"
      urls = [url + ("coco2017labels-segments.zip" if segments else "coco2017labels.zip")]  # labels
      download(urls, dir=dir.parent)
      # Download data
      urls = [
          "http://images.cocodataset.org/zips/train2017.zip",  # 19G, 118k images
          "http://images.cocodataset.org/zips/val2017.zip",  # 1G, 5k images
          "http://images.cocodataset.org/zips/test2017.zip",  # 7G, 41k images (optional)
      ]
      download(urls, dir=dir / "images", threads=3)

## Usage

To train a YOLO11n-seg model on the COCO-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.

Train Example

Python CLI

    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)

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

    # Start training from a pretrained *.pt model
    yolo segment train data=coco.yaml model=yolo11n-seg.pt epochs=100 imgsz=640

## Sample Images and Annotations

COCO-Seg, like its predecessor COCO, contains a diverse set of images with various object categories and complex scenes. However, COCO-Seg introduces more detailed instance segmentation masks for each object in the images. Here are some examples of images from the dataset, along with their corresponding instance segmentation masks:

- 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 aids the model's ability to generalize to different object sizes, aspect ratios, and contexts.

The example showcases the variety and complexity of the images in the COCO-Seg dataset and the benefits of using mosaicing during the training process.

## Citations and Acknowledgments

If you use the COCO-Seg dataset in your research or development work, please cite the original COCO paper and acknowledge the extension to COCO-Seg:

BibTeX

    @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 extend our thanks to the COCO Consortium for creating and maintaining this invaluable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the COCO dataset website.

## FAQ

### What is the COCO-Seg dataset and how does it differ from the original COCO dataset?

The COCO-Seg dataset is an extension of the original COCO (Common Objects in Context) dataset, specifically designed for instance segmentation tasks. While it uses the same images as the COCO dataset, COCO-Seg includes more detailed segmentation annotations, making it a powerful resource for researchers and developers focusing on object instance segmentation.

### How can I train a YOLO11 model using the COCO-Seg dataset?

To train a YOLO11n-seg model on the COCO-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a detailed list of available arguments, refer to the model Training page.

Train Example

Python CLI

    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)

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

    # Start training from a pretrained *.pt model
    yolo segment train data=coco.yaml model=yolo11n-seg.pt epochs=100 imgsz=640

### What are the key features of the COCO-Seg dataset?

The COCO-Seg dataset includes several key features:

- Retains the original 330K images from the COCO dataset.
- Annotates the same 80 object categories found in the original COCO.
- Provides more detailed instance segmentation masks for each object.
- Uses standardized evaluation metrics such as mean Average Precision (mAP) for object detection and mean Average Recall (mAR) for instance segmentation tasks.

### What pretrained models are available for COCO-Seg, and what are their performance metrics?

The COCO-Seg dataset supports multiple pretrained YOLO11 segmentation models with varying performance metrics. Here's a summary of the available models and their key metrics:

Model | size (pixels) | mAPbox 50-95 | mAPmask 50-95 | Speed CPU ONNX (ms) | Speed T4 TensorRT10 (ms) | params (M) | FLOPs (B)
--- | --- | --- | --- | --- | --- | --- | ---
YOLO11n-seg | 640 | 38.9 | 32.0 | 65.9 ± 1.1 | 1.8 ± 0.0 | 2.9 | 10.4
YOLO11s-seg | 640 | 46.6 | 37.8 | 117.6 ± 4.9 | 2.9 ± 0.0 | 10.1 | 35.5
YOLO11m-seg | 640 | 51.5 | 41.5 | 281.6 ± 1.2 | 6.3 ± 0.1 | 22.4 | 123.3
YOLO11l-seg | 640 | 53.4 | 42.9 | 344.2 ± 3.2 | 7.8 ± 0.2 | 27.6 | 142.2
YOLO11x-seg | 640 | 54.7 | 43.8 | 664.5 ± 3.2 | 15.8 ± 0.7 | 62.1 | 319.0

These models range from the lightweight YOLO11n-seg to the more powerful YOLO11x-seg, offering different trade-offs between speed and accuracy to suit various application requirements. For more information on model selection, visit the Ultralytics models page.

### How is the COCO-Seg dataset structured and what subsets does it contain?

The COCO-Seg dataset is partitioned into three subsets for specific training and evaluation needs:

1. Train2017: Contains 118K images used primarily for training instance segmentation models.
2. Val2017: Comprises 5K images utilized for validation during the training process.
3. Test2017: Encompasses 20K images reserved for testing and benchmarking trained models. Note that ground truth annotations for this subset are not publicly available, and performance results are submitted to the COCO evaluation server for assessment.

For smaller experimentation needs, you might also consider using the COCO8-seg dataset, which is a compact version containing just 8 images from the COCO train 2017 set.

📅 Created 1 year ago ✏️ Updated 6 months ago

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