In July 2022, WongKinYiu and AlexeyAB released YOLOv7, an update to the popular lineage of YOLO-based models. It's an improvement in both speed and accuracy over previous YOLO models.
Roboflow supports converting 30+ different object detection annotation formats into the TXT format that YOLOv7 needs and we automatically generate your YAML config file for you. Plus, all 90,000+ datasets available on Roboflow Universe are available in YOLOv7 format for seamless use in custom training.
Check out our breakdown of YOLOv7 or train YOLOv7 on your custom dataset.
Each image has one txt file with a single line for each bounding box. The format of each row is
class_id center_x center_y width height
where fields are space delimited, and the coordinates are normalized from zero to one.
Note: To convert to normalized xywh from pixel values, divide x (and width) by the image's width and divide y (and height) by the image's height.
1 0.617 0.3594420600858369 0.114 0.17381974248927037
1 0.094 0.38626609442060084 0.156 0.23605150214592274
1 0.295 0.3959227467811159 0.13 0.19527896995708155
1 0.785 0.398068669527897 0.07 0.14377682403433475
1 0.886 0.40879828326180256 0.124 0.18240343347639484
1 0.723 0.398068669527897 0.102 0.1609442060085837
1 0.541 0.35085836909871243 0.094 0.16952789699570817
1 0.428 0.4334763948497854 0.068 0.1072961373390558
1 0.375 0.40236051502145925 0.054 0.1351931330472103
1 0.976 0.3927038626609442 0.044 0.17167381974248927The `data.yaml` file contains configuration values used by the model to locate images and map class names to class_id's.
train: ../train/images
val: ../valid/images
nc: 3
names: ['head', 'helmet', 'person']
On each page below, you can find links to our guides that show how to plot predictions from the model, and complete other common tasks like detecting small objects with the model.
