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110 lines
3.3 KiB
110 lines
3.3 KiB
---
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comments: true
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description: Learn about the Ultralytics YOLO dataset format for segmentation models. Use YAML to train Detection Models. Convert COCO to YOLO format using Python.
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---
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# Instance Segmentation Datasets Overview
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## Supported Dataset Formats
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### Ultralytics YOLO format
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** Label Format **
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The dataset format used for training YOLO segmentation models is as follows:
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1. One text file per image: Each image in the dataset has a corresponding text file with the same name as the image file and the ".txt" extension.
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2. One row per object: Each row in the text file corresponds to one object instance in the image.
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3. Object information per row: Each row contains the following information about the object instance:
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- Object class index: An integer representing the class of the object (e.g., 0 for person, 1 for car, etc.).
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- Object bounding coordinates: The bounding coordinates around the mask area, normalized to be between 0 and 1.
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The format for a single row in the segmentation dataset file is as follows:
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```
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<class-index> <x1> <y1> <x2> <y2> ... <xn> <yn>
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```
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In this format, `<class-index>` is the index of the class for the object, and `<x1> <y1> <x2> <y2> ... <xn> <yn>` are the bounding coordinates of the object's segmentation mask. The coordinates are separated by spaces.
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Here is an example of the YOLO dataset format for a single image with two object instances:
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```
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0 0.6812 0.48541 0.67 0.4875 0.67656 0.487 0.675 0.489 0.66
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1 0.5046 0.0 0.5015 0.004 0.4984 0.00416 0.4937 0.010 0.492 0.0104
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```
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Note: The length of each row does not have to be equal.
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** Dataset file format **
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The Ultralytics framework uses a YAML file format to define the dataset and model configuration for training Detection Models. Here is an example of the YAML format used for defining a detection dataset:
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```yaml
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train: <path-to-training-images>
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val: <path-to-validation-images>
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nc: <number-of-classes>
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names: [<class-1>, <class-2>, ..., <class-n>]
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```
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The `train` and `val` fields specify the paths to the directories containing the training and validation images, respectively.
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The `nc` field specifies the number of object classes in the dataset.
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The `names` field is a list of the names of the object classes. The order of the names should match the order of the object class indices in the YOLO dataset files.
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NOTE: Either `nc` or `names` must be defined. Defining both are not mandatory.
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Alternatively, you can directly define class names like this:
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```yaml
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names:
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0: person
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1: bicycle
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```
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** Example **
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```yaml
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train: data/train/
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val: data/val/
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nc: 2
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names: ['person', 'car']
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```
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## Usage
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-seg.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data='coco128-seg.yaml', epochs=100, imgsz=640)
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```
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=== "CLI"
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```bash
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# Start training from a pretrained *.pt model
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yolo detect train data=coco128-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
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```
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## Supported Datasets
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## Port or Convert label formats
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### COCO dataset format to YOLO format
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```
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from ultralytics.yolo.data.converter import convert_coco
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convert_coco(labels_dir='../coco/annotations/', use_segments=True)
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``` |