CoolFace
Modelpublic

Yifeng-Liu/rt-detr-finetuned-for-satellite-image-roofs-detection

sourceHugging Facemitupdated 25d agoView on Hugging Face
18likes2.6kdownloads
Model Card

Model Card

Roof detection model for remote sensing imagery, fine-tuned using RT-DETR. <!-- Provide a quick summary of what the model is/does. -->

Example Prediction

The following example shows roof detections produced by the model:

[image]

Model Details

Model Description

<!-- Provide a longer summary of what this model is. -->

  • —Model type: Object Detection for Remote Sensing task.
  • —License: MIT

Model Sources

<!-- Provide the basic links for the model. -->

Try it

Limitations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

How to Get Started with the Model

Use the code below to get started with the model.

python
from transformers import AutoModelForObjectDetection, AutoImageProcessor
import torch
import cv2

image_path=YOUR_IMAGE_PATH
image = cv2.imread(image_path)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = AutoModelForObjectDetection.from_pretrained("Yifeng-Liu/rt-detr-finetuned-for-satellite-image-roofs-detection")
image_processor = AutoImageProcessor.from_pretrained("Yifeng-Liu/rt-detr-finetuned-for-satellite-image-roofs-detection")


CONFIDENCE_TRESHOLD = 0.5

with torch.no_grad():
    model.to(device)

    # load image and predict
    inputs = image_processor(images=image, return_tensors='pt').to(device)
    outputs = model(**inputs)

    # post-process
    target_sizes = torch.tensor([image.shape[:2]]).to(device)
    results = image_processor.post_process_object_detection(
        outputs=outputs,
        threshold=CONFIDENCE_TRESHOLD,
        target_sizes=target_sizes
    )[0]