Dataset Formats

RF-DETR supports training on datasets in two popular formats: COCO and YOLO. The format is automatically detected based on your dataset’s directory structure — simply pass your dataset directory to the train() method.

Automatic format detection

When you call model.train(dataset_dir="<path>"), RF-DETR checks the following:

COCO format: Looks for train/_annotations.coco.json
YOLO format: Looks for data.yaml (or data.yml) and train/images/ directory

If neither format is detected, an error is raised with instructions on what’s expected.

Roboflow can export datasets in both COCO and YOLO formats. You can also explore Roboflow Universe to find pre-labeled datasets for a range of use cases.

Format structures

COCO format
YOLO format

COCO (Common Objects in Context) format uses JSON files to store annotations in a structured format with images, categories, and annotations.

Directory structure

dataset/
├── train/
│   ├── _annotations.coco.json
│   ├── image1.jpg
│   ├── image2.jpg
│   └── ...
├── valid/
│   ├── _annotations.coco.json
│   ├── image1.jpg
│   └── ...
└── test/
    ├── _annotations.coco.json
    ├── image1.jpg
    └── ...

Annotation file structure

Each _annotations.coco.json file contains:

{
  "info": {
    "description": "Dataset description",
    "version": "1.0"
  },
  "licenses": [],
  "images": [
    {
      "id": 1,
      "file_name": "image1.jpg",
      "width": 640,
      "height": 480
    }
  ],
  "categories": [
    {
      "id": 1,
      "name": "cat",
      "supercategory": "animal"
    },
    {
      "id": 2,
      "name": "dog",
      "supercategory": "animal"
    }
  ],
  "annotations": [
    {
      "id": 1,
      "image_id": 1,
      "category_id": 1,
      "bbox": [100, 150, 200, 180],
      "area": 36000,
      "iscrowd": 0
    }
  ]
}

Field	Description
`images`	List of image metadata including `id`, `file_name`, `width`, `height`
`categories`	List of object categories with `id` and `name`
`annotations`	List of object annotations linking images to categories
`bbox`	Bounding box in `[x, y, width, height]` format (top-left corner)
`area`	Area of the bounding box
`iscrowd`	`0` for individual objects, `1` for crowd regions

Segmentation annotations

For training segmentation models, your COCO annotations must include a segmentation key with polygon coordinates:

{
  "id": 1,
  "image_id": 1,
  "category_id": 1,
  "bbox": [100, 150, 200, 180],
  "area": 36000,
  "iscrowd": 0,
  "segmentation": [
    [100, 150, 150, 150, 200, 200, 150, 250, 100, 200]
  ]
}

The segmentation field contains a list of polygons, where each polygon is a flat list of coordinates: [x1, y1, x2, y2, x3, y3, ...].

YOLO format uses separate text files for each image’s annotations and a data.yaml configuration file that defines class names.

Directory structure

dataset/
├── data.yaml
├── train/
│   ├── images/
│   │   ├── image1.jpg
│   │   └── ...
│   └── labels/
│       ├── image1.txt
│       └── ...
├── valid/
│   ├── images/
│   │   └── ...
│   └── labels/
│       └── ...
└── test/
    ├── images/
    │   └── ...
    └── labels/
        └── ...

data.yaml configuration

The data.yaml file at the root of your dataset directory defines the class names:

names:
  - cat
  - dog
  - bird

nc: 3

train: train/images
val: valid/images
test: test/images

Field	Description
`names`	List of class names (0-indexed)
`nc`	Number of classes
`train`, `val`, `test`	Paths to image directories (relative to data.yaml)

Some YOLO datasets use a dictionary format for names:

names:
cat
dog
bird

Both formats are supported.

Label file format

Each image has a corresponding .txt file in the labels/ directory with the same base name. Each line represents one object:

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

Example image1.txt:

0 0.5 0.4 0.3 0.2
1 0.2 0.6 0.15 0.25

All coordinates are normalized relative to image dimensions:

Field	Range	Description
`class_id`	0, 1, 2, …	Zero-indexed class ID from `names` in data.yaml
`x_center`	0.0–1.0	Normalized x-coordinate of bounding box center
`y_center`	0.0–1.0	Normalized y-coordinate of bounding box center
`width`	0.0–1.0	Normalized width of bounding box
`height`	0.0–1.0	Normalized height of bounding box

Segmentation labels (YOLO-Seg)

For segmentation, YOLO format extends the label format with polygon coordinates:

<class_id> <x1> <y1> <x2> <y2> <x3> <y3> ...

Example image1.txt with segmentation:

0 0.1 0.2 0.3 0.2 0.4 0.5 0.2 0.6 0.1 0.4

Converting between formats

Use the supervision library to convert between COCO and YOLO formats:

YOLO to COCO
COCO to YOLO

import supervision as sv

# Load YOLO dataset
dataset = sv.DetectionDataset.from_yolo(
    images_directory_path="path/to/images",
    annotations_directory_path="path/to/labels",
    data_yaml_path="path/to/data.yaml",
)

# Save as COCO
dataset.as_coco(
    images_directory_path="output/images",
    annotations_path="output/annotations.json",
)

import supervision as sv

# Load COCO dataset
dataset = sv.DetectionDataset.from_coco(
    images_directory_path="path/to/images",
    annotations_path="path/to/annotations.json",
)

# Save as YOLO
dataset.as_yolo(
    images_directory_path="output/images",
    annotations_directory_path="output/labels",
    data_yaml_path="output/data.yaml",
)

Which format should I use?

Both formats work equally well with RF-DETR. Choose based on your workflow:

Consideration	COCO	YOLO
Annotation storage	Single JSON file per split	One text file per image
Human readability	JSON structure, verbose	Simple text, compact
Other framework compatibility	DETR family, MMDetection	Ultralytics YOLO
Segmentation support	Full polygon support	Full polygon support
Editing annotations	Requires JSON parsing	Simple text editing

If you’re exporting from Roboflow or already have a dataset in one format, simply use that format. RF-DETR handles both identically.

Troubleshooting

Format detection fails

If you see an error like Could not detect dataset format in /path/to/dataset, check: For COCO format:

train/_annotations.coco.json exists
The JSON file is valid

For YOLO format:

data.yaml or data.yml exists at the root
train/images/ directory exists with images

Empty annotations

COCO format: Include the image in the images array but don’t add any annotations for it. YOLO format: Create an empty .txt file (0 bytes) for the image, or omit the label file entirely.

Class ID mismatch

COCO format: Category IDs in annotations must match IDs defined in the categories array. YOLO format: Class IDs in label files must be valid indices (0 to nc-1) based on the names list in data.yaml.

Get Started

Run Models

Train Models

Deploy & Export

Dataset Formats

Automatic format detection

Format structures

Directory structure

Annotation file structure

Segmentation annotations

Directory structure

data.yaml configuration

Label file format

Segmentation labels (YOLO-Seg)

Converting between formats

Which format should I use?

Troubleshooting

Format detection fails

Empty annotations

Class ID mismatch

Build docs developers (and LLMs) love

Get Started

Run Models

Train Models

Deploy & Export

​Automatic format detection

​Format structures

​Directory structure

​Annotation file structure

​Segmentation annotations

​Directory structure

​data.yaml configuration

​Label file format

​Segmentation labels (YOLO-Seg)

​Converting between formats

​Which format should I use?

​Troubleshooting

​Format detection fails

​Empty annotations

​Class ID mismatch

Build docs developers (and LLMs) love

Automatic format detection

Format structures

Directory structure

Annotation file structure

Segmentation annotations

Directory structure

data.yaml configuration

Label file format

Segmentation labels (YOLO-Seg)

Converting between formats

Which format should I use?

Troubleshooting

Format detection fails

Empty annotations

Class ID mismatch