退购1.1定位算法

docs/datasets/detect/index.md

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+---
+comments: true
+description: Learn about supported dataset formats for training YOLO detection models, including Ultralytics YOLO and COCO, in this Object Detection Datasets Overview.
+---
+
+# Object Detection Datasets Overview
+
+## Supported Dataset Formats
+
+### Ultralytics YOLO format
+
+** Label Format **
+
+The dataset format used for training YOLO detection models is as follows:
+
+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.
+2. One row per object: Each row in the text file corresponds to one object instance in the image.
+3. Object information per row: Each row contains the following information about the object instance:
+    - Object class index: An integer representing the class of the object (e.g., 0 for person, 1 for car, etc.).
+    - Object center coordinates: The x and y coordinates of the center of the object, normalized to be between 0 and 1.
+    - Object width and height: The width and height of the object, normalized to be between 0 and 1.
+
+The format for a single row in the detection dataset file is as follows:
+
+```
+<object-class> <x> <y> <width> <height>
+```
+
+Here is an example of the YOLO dataset format for a single image with two object instances:
+
+```
+0 0.5 0.4 0.3 0.6
+1 0.3 0.7 0.4 0.2
+```
+
+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.
+
+** Dataset file format **
+
+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:
+
+```
+train: <path-to-training-images>
+val: <path-to-validation-images>
+
+nc: <number-of-classes>
+names: [<class-1>, <class-2>, ..., <class-n>]
+
+```
+
+The `train` and `val` fields specify the paths to the directories containing the training and validation images, respectively.
+
+The `nc` field specifies the number of object classes in the dataset.
+
+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.
+
+NOTE: Either `nc` or `names` must be defined. Defining both are not mandatory
+
+Alternatively, you can directly define class names like this:
+
+```yaml
+names:
+  0: person
+  1: bicycle
+```
+
+** Example **
+
+```yaml
+train: data/train/
+val: data/val/
+
+nc: 2
+names: ['person', 'car']
+```
+
+## Usage
+
+!!! example ""
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+        
+        # Load a model
+        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        model.train(data='coco128.yaml', epochs=100, imgsz=640)
+        ```
+    === "CLI"
+    
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Supported Datasets
+
+TODO
+
+## Port or Convert label formats
+
+### COCO dataset format to YOLO format
+
+```
+from ultralytics.yolo.data.converter import convert_coco
+
+convert_coco(labels_dir='../coco/annotations/')
+```