owlv2-base-patch16 EU AI Act Compliance Profile

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-B/16 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. The CLIP backbone is trained from scratch and fine-tuned together with the box and class prediction heads with an object detection objective.

Documents

• OWLv2 Paper

Use with Transformers

import requests
from PIL import Image
import numpy as np
import torch
from transformers import AutoProcessor, Owlv2ForObjectDetection
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD

processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16")
model = Owl

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