omdet turbo swin tiny hf EU AI Act Compliance Profile

OmDet model

The OmDet model was proposed in Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head by Tiancheng Zhao, Peng Liu, Xuan He, Lu Zhang, Kyusong Lee from Om AI Lab.

Github Repository

If you like our model, please consider following our project on GitHub OmDet to receive updates and information about new model releases.

We also invite you to explore our latest work on the Agents Framework OmAgent.

Intended use cases

This model is intended for zero-shot (also called open-vocabulary) object detection.

Usage

Single image inference

Here's how to load the model and prepare the inputs to perform zero-shot object detection on a single image:

import requests
from PIL import Image

from transformers import AutoProcessor, OmDetTurboForObjectDetection

processor = AutoProcessor.from_pretrained("omlab/omdet-turbo-swin-tiny-hf")
model = OmDetTurboForObjectDetection.from_pretrained("omlab/omdet-turbo-swin-tiny-hf")

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

outputs = model(**inputs)

# convert outputs (bounding boxes and class logits)
results = processor.post_process_grounded_object_detection(
    outputs,
    classes=classes,
    target_sizes=[image.size[::-1]],
    score_threshold=0.3,
    nms_threshold=0.3,
)[0]
for score, class_name, box in zip(
    results["scores"], results["classes"], results["boxes"]
):
    box = [round(i, 1) for i in box.tolist()]
    print(
        f"Detected {class_name} with confidence "
        f"{round(score.item(), 2)} at location {box}"
    )

Batched images inference

OmDet-Turbo can perform batched multi-image inference, with support for different text prompts and classes in the same batch:

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