YOLOS looks at patches of an image to to form "patch tokens", which are used in place of the traditional wordpiece tokens in NLP.
Here is an overview of the
model:
YOLOS looks at patches of an image to to form "patch tokens", which are used in place of the traditional wordpiece tokens in NLP. There are 100 detection tokens on the right are learnable embeddings and feed into potential detections.
Compared to other CNN-based YOLO models, YOLOS benefits from the rising tides of transformers in computer vision, as well as inferring without the need for NMS, a tedious post-processing step that makes the deployment of other YOLO models difficult and slow.
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.
YOLOS
uses the
COCO JSON
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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