Model Card: OWLv2 Model Details The OWLv2 model (short for Open World Localization) was proposed in Scaling Open Vocabulary Object Detection by Matthias Minderer, Alexey Gritsenko, Neil Houlsby. OWLv2, like 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. The model 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 matching loss. One or multiple text queries per image can be used to perform zero shot text conditioned object detection. Model Date June 2023 Model Type The model uses a CLIP backbone with a ViT L/14 Transforme…
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