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

Model Card

Roof Detection for Remote Sensing task.

Model Details

Model Description

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

Model Sources

• GitHub: Jupyter Notebook
• Demo: [Pending]

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.

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]