Model Card: OWL ViT Model Details The OWL ViT (short for Vision Transformer for Open World Localization) was proposed in Simple Open Vocabulary Object Detection with Vision Transformers by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby. OWL ViT is a zero shot text conditioned object detection model that can be used to query an image with one or multiple text queries. OWL ViT uses CLIP as its multi modal backbone, with a ViT like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL ViT removes the final token pooling layer of the vision model and attaches a lightweight classification and box head to each transformer output token. Open vocabulary classification is enabled by replacing the fixed classification layer weights with the class name embeddings obtained from the text model. The authors first train CLIP from scratch and fine tune it end to end with the classification and box heads on standard detection datasets using a bipartite matchi…
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