YOLOv7 is a state of the art object detection model.
Here is an overview of the
model:
YOLOv7 was released in July 2022 by WongKinYiu and AlexeyAB. It achieves state of the art performance on and are trained to detect the generic 80 classes in the MS COCO dataset for real-time object detection.
There are six versions of the model ranging from the namesake YOLOv7 (fastest, smallest, and least accurate) to the beefy YOLOv7-E6E (slowest, largest, and most accurate).
The differences between the different sizes of the model are:
The evaluation of YOLOv7 models show that they infer faster (x-axis) and with greater accuracy (y-axis) than comparable realtime object detection models. YOLOv7 evaluates in the upper left - faster and more accurate than its peer networks.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv7
uses the
YOLOv7 PyTorch TXT
annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.
Curious about how YOLOv5 compares to other models? Check out our model comparisons.
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