YOLOS (base sized) model YOLOS model fine tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository. Disclaimer: The team releasing YOLOS did not write a model card for this model so this model card has been written by the Hugging Face team. Model description YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R CNN). The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one to one mapping between each of the N queries and each of the N annotations. Next, standard cross entropy (for the classes)…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy