# Yolo ## Overview This converter allows to import images with annotations in [YOLO](https://docs.ultralytics.com/datasets/detect/) format for **segmentation**, **detection** and **pose estimation** tasks. Each image should have a corresponding `.txt` file with the same name, which contains information about objects in the image. * Segmentation labels will be converted to polygons. Labels format: ` ... ` * Detection labels will be converted to rectangles. Labels format: ` ` * Pose estimation labels will be converted to keypoints. Labels format: ` ... ` for Dim=2 and ` ` for Dim=3. YOLO format data should have a specific configuration file that contains information about classes and datasets, usually named `data_config.yaml`. ⚠️ **Note:** If the input data does not contain `data_config.yaml` file, it will use default COCO class names. ![Result of the import](https://1080806899-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-M4BHwRbuyIoH-xoF3Gv%2Fuploads%2Fgit-blob-ccdead91c27074a8dc3577c6c6402158d9828f2f%2Fyolo_res.png?alt=media) Enterprise users have access to "Import as links" option, which supports import of this format with annotations. This option might be beneficial in many cases, as it allows data import to Supervisely platform without re-uploading, maintaining a single source and speeding up import process. To step up import speed even further you can compress all annotation files (`.txt`'s) into an archive and import it together with the images. (Note: This method is format-dependent and may not apply to all formats.)
Default COCO class names ``` names: [ "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush", ] ```
## Format description **Supported image formats:** `.jpg`, `.jpeg`, `.mpo`, `.bmp`, `.png`, `.webp`, `.tiff`, `.tif`, `.jfif`, `.avif`, `.heic`, and `.heif`\ **With annotations:** Yes\ **Supported annotation file extension:** `.txt`.\ **Grouped by:** Any structure (will be uploaded as a single dataset)\\ ## Input files structure {% hint style="success" %} Example data: [download ⬇️](https://github.com/supervisely-ecosystem/import-wizard-docs/files/14919196/sample_yolo.zip)\\ {% endhint %} Recommended directory structure: ``` 📂project name ┣ 📂images ┃ ┣ 📂train ┃ ┃ ┣ 🖼️IMG_0748.jpeg ┃ ┃ ┣ 🖼️IMG_1836.jpeg ┃ ┃ ┣ 🖼️IMG_2084.jpeg ┃ ┃ ┗ 🖼️IMG_3861.jpeg ┃ ┗ 📂val ┃ ┣ 🖼️IMG_4451.jpeg ┃ ┗ 🖼️IMG_8144.jpeg ┣ 📂labels ┃ ┣ 📂train ┃ ┃ ┣ 📜IMG_0748.txt ┃ ┃ ┣ 📜IMG_1836.txt ┃ ┃ ┣ 📜IMG_2084.txt ┃ ┃ ┗ 📜IMG_3861.txt ┃ ┗ 📂val ┃ ┣ 📜IMG_4451.txt ┃ ┗ 📜IMG_8144.txt ┗ 📜data_config.yaml ``` ## Format Config File File `data_config.yaml` should contain the following keys: * `names` - a list of class names * `colors` - a list of class colors in RGB format * `nc` - the number of classes * `train` - the path to the train images * `val` - the path to the validation images
📜data_config.yaml ```yaml names: [kiwi, lemon] # class names colors: [[255, 1, 1], [1, 255, 1]] # class colors nc: 2 # number of classes train: ../lemons/images/train # path to train imgs (or "images/train") val: ../lemons/images/val # path to val imgs (or "images/val") # Keypoints (for pose estimation) kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible) ```
## Single-Image Annotation Annotation files are in `.txt` format and should contain object labels on each line: * Class numbers that correspond to the class names in the `data_config.yaml` file. * Label coordinates must be in normalized format (from 0 to 1). **1. Segmentation** Labels should be formatted with one row per object in: ``` ... ``` **2. Detection:** Labels should be formatted with one row per object in: ``` ``` If your boxes are in pixels, you should divide x\_center and width by image width, and y\_center and height by image height. **3. Pose Estimation:** Labels should be formatted with one row per object. For Dim=2: ``` ... ``` For Dim=3: ``` ... ``` **Yolo coordinates explanation:** The label file corresponding to the below image contains 2 persons (class 0) and a tie (class 27) from original COCO classes. 📜zidan.txt: ``` 0 0.481719 0.634028 0.690625 0.713278 0 0.741094 0.524306 0.314750 0.933389 27 0.364844 0.795833 0.078125 0.400000 ``` ![Yolo coordinates explanation](https://1080806899-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-M4BHwRbuyIoH-xoF3Gv%2Fuploads%2Fgit-blob-8fe38a47303a72701a35d894d9fb9e48416f63c7%2Fyolo_coords.png?alt=media) ## Useful links * [YOLO format](https://docs.ultralytics.com/datasets/detect/) * [\[Supervisely Ecosystem\] Convert YOLO v5 to Supervisely format](https://ecosystem.supervisely.com/apps/convert-yolov5-to-supervisely-format)