In 2022, MT-YOLOv6 was released, which is an iteration on the YOLO family of models; it was created by a new author and is not meant to be the direct successor to YOLOv4 or YOLOv5.
With Roboflow, you can deploy a computer vision model without having to build your own infrastructure.
Below, we show how to convert data to and from
meituan/yolov6
. We also list popular models that use the
meituan/yolov6
data format. Our conversion tools are free to use.
Free data conversion
SOC II Type 2 Compliant
Trusted by 250,000+ developers
Free data conversion
SOC II Type 1 Compliant
Trusted by 250,000+ developers
The
MT-YOLOv6
,
models all use the
data format.
The annotation format is the same as YOLOv5 but with changes to the YAML file. 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 0.17167381974248927
The `data.yaml` file contains configuration values used by the model to locate images and map class names to class_id
's.
train: ./images/train
val: ./images/valid
test: ./images/test
nc: 3
names: ['head', 'helmet', 'person']