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111 lines
3.4 KiB
111 lines
3.4 KiB
---
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comments: true
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description: Learn about supported dataset formats for training YOLO detection models, including Ultralytics YOLO and COCO, in this Object Detection Datasets Overview.
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keywords: object detection, datasets, formats, Ultralytics YOLO, label format, dataset file format, dataset definition, YOLO dataset, model configuration
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---
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# Object Detection 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 detection 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 center coordinates: The x and y coordinates of the center of the object, normalized to be between 0 and 1.
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- Object width and height: The width and height of the object, normalized to be between 0 and 1.
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The format for a single row in the detection dataset file is as follows:
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```
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<object-class> <x> <y> <width> <height>
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```
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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 0.5 0.4 0.3 0.6
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1 0.3 0.7 0.4 0.2
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```
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In this example, the first object is of class 0 (person), with its center at (0.5, 0.4), width of 0.3, and height of 0.6. The second object is of class 1 (car), with its center at (0.3, 0.7), width of 0.4, and height of 0.2.
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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.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data='coco128.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.yaml model=yolov8n.pt epochs=100 imgsz=640
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```
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## Supported Datasets
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TODO
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## Port or Convert label formats
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### COCO dataset format to YOLO format
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```python
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from ultralytics.yolo.data.converter import convert_coco
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convert_coco(labels_dir='../coco/annotations/')
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``` |