Omdet Turbo Swin Tiny Hf

🚀 OmDet model

The OmDet model is a zero - shot object detection model. It 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. It offers a solution for detecting objects without pre - defined classes in real - time scenarios.

🚀 Quick Start

Intended Use Cases

This model is designed for zero - shot (also known as open - vocabulary) object detection, enabling the detection of objects without the need for pre - defined classes.

Usage

Basic Usage

Here are the steps to perform zero - shot object detection on a single image using the OmDet model:

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}"
    )

Advanced Usage

OmDet - Turbo can handle batched multi - image inference, supporting different text prompts and classes in the same batch:

>>> import torch
>>> import requests
>>> from io import BytesIO
>>> 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")

>>> url1 = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image1 = Image.open(BytesIO(requests.get(url1).content)).convert("RGB")
>>> classes1 = ["cat", "remote"]
>>> task1 = "Detect {}.".format(", ".join(classes1))

>>> url2 = "http://images.cocodataset.org/train2017/000000257813.jpg"
>>> image2 = Image.open(BytesIO(requests.get(url2).content)).convert("RGB")
>>> classes2 = ["boat"]
>>> task2 = "Detect everything that looks like a boat."

>>> url3 = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
>>> image3 = Image.open(BytesIO(requests.get(url3).content)).convert("RGB")
>>> classes3 = ["statue", "trees"]
>>> task3 = "Focus on the foreground, detect statue and trees."

>>> inputs = processor(
...     images=[image1, image2, image3],
...     text=[classes1, classes2, classes3],
...     task=[task1, task2, task3],
...     return_tensors="pt",
... )

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

>>> # convert outputs (bounding boxes and class logits)
>>> results = processor.post_process_grounded_object_detection(
...     outputs,
...     classes=[classes1, classes2, classes3],
...     target_sizes=[image1.size[::-1], image2.size[::-1], image3.size[::-1]],
...     score_threshold=0.2,
...     nms_threshold=0.3,
... )

>>> for i, result in enumerate(results):
...     for score, class_name, box in zip(
...         result["scores"], result["classes"], result["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} in image {i}"
...         )
Detected remote with confidence 0.77 at location [39.9, 70.4, 176.7, 118.0] in image 0
Detected cat with confidence 0.72 at location [11.6, 54.2, 314.8, 474.0] in image 0
Detected remote with confidence 0.56 at location [333.4, 75.8, 370.7, 187.0] in image 0
Detected cat with confidence 0.55 at location [345.2, 24.0, 639.8, 371.7] in image 0
Detected boat with confidence 0.32 at location [146.9, 219.8, 209.6, 250.7] in image 1
Detected boat with confidence 0.3 at location [319.1, 223.2, 403.2, 238.4] in image 1
Detected boat with confidence 0.27 at location [37.7, 220.3, 84.0, 235.9] in image 1
Detected boat with confidence 0.22 at location [407.9, 207.0, 441.7, 220.2] in image 1
Detected statue with confidence 0.73 at location [544.7, 210.2, 651.9, 502.8] in image 2
Detected trees with confidence 0.25 at location [3.9, 584.3, 391.4, 785.6] in image 2
Detected trees with confidence 0.25 at location [1.4, 621.2, 118.2, 787.8] in image 2
Detected statue with confidence 0.2 at location [428.1, 205.5, 767.3, 759.5] in image 2

📚 Documentation

Github Repository

If you're interested in our model, please consider following our project on GitHub OmDet to stay updated on new model releases and other information.

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

📄 License

This project is licensed under the Apache - 2.0 license.