Owlv2 Large Patch14 Ensemble

🚀 Model Card: OWLv2

The OWLv2 model is a zero-shot text-conditioned object detection model. It can identify objects in images using text queries, offering a flexible solution for open-world object detection tasks.

🚀 Quick Start

Use with Transformers

import requests
from PIL import Image
import torch

from transformers import Owlv2Processor, Owlv2ForObjectDetection

processor = Owlv2Processor.from_pretrained("google/owlv2-large-patch14-ensemble")
model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-large-patch14-ensemble")

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
texts = [["a photo of a cat", "a photo of a dog"]]
inputs = processor(text=texts, images=image, return_tensors="pt")

with torch.no_grad():
  outputs = model(**inputs)

# Target image sizes (height, width) to rescale box predictions [batch_size, 2]
target_sizes = torch.Tensor([image.size[::-1]])
# Convert outputs (bounding boxes and class logits) to Pascal VOC Format (xmin, ymin, xmax, ymax)
results = processor.post_process_object_detection(outputs=outputs, target_sizes=target_sizes, threshold=0.1)
i = 0  # Retrieve predictions for the first image for the corresponding text queries
text = texts[i]
boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"]
for box, score, label in zip(boxes, scores, labels):
    box = [round(i, 2) for i in box.tolist()]
    print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")

✨ Features

• Zero-shot Detection: Can perform object detection using text queries without prior training on specific object classes.
• Open-vocabulary: Enables detection of objects with any text description.
• Multi-modal Backbone: Uses CLIP to combine visual and text features effectively.

📚 Documentation

• OWLv2 Paper

🔧 Technical Details

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

📄 License

The model is licensed under the Apache-2.0 license.

BibTeX entry and citation info

@misc{minderer2023scaling,
      title={Scaling Open-Vocabulary Object Detection}, 
      author={Matthias Minderer and Alexey Gritsenko and Neil Houlsby},
      year={2023},
      eprint={2306.09683},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Model Use

Intended Use

The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, text-conditioned object detection. We also hope it can be used for interdisciplinary studies of the potential impact of such models, especially in areas that commonly require identifying objects whose label is unavailable during training.

Primary intended uses

The primary intended users of these models are AI researchers.

We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.