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Two-stage object detection models with Pytorch usually consist of a region proposal network (RPN), implementing with faster R-CNN

Tablet50 answered on June 19, 2023 Popularity 1/10 Helpfulness 1/10

answer Two-stage object detection models with Pytorch usually consist of a region proposal network (RPN), implementing with faster R-CNN

Two-stage object detection models with Pytorch usually consist of a region proposal network (RPN), implementing with faster R-CNN

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# two-stage object detection models in Faster R-CNN. implementation in using PyTorch
import torch
from torchvision.models.detection import fasterrcnn_resnet50_fpn
from torchvision.transforms import functional as F
from PIL import Image

# Ensure we're using the GPU if available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Load a pre-trained Faster R-CNN ResNet-50 FPN model
model = fasterrcnn_resnet50_fpn(pretrained=True)
model = model.to(device)  # move model to the right device
model.eval()  # set model to evaluation mode

def predict(image_path):
    # Load the image
    image = Image.open(image_path).convert("RGB")
    image = F.to_tensor(image).unsqueeze(0).to(device)  # transform image and move to device

    with torch.no_grad():
        prediction = model(image)  # forward pass image through the model
    
    # Get bounding boxes and labels with a high confidence score
    boxes, labels = [], []
    for box, label, score in zip(prediction[0]['boxes'], prediction[0]['labels'], prediction[0]['scores']):
        if score > 0.5:
            boxes.append(box.tolist())
            labels.append(label.item())

    return boxes, labels

# Test the function with an image
boxes, labels = predict("image.jpg")


# This code is similar to the one-stage detector template.
# However, Faster R-CNN is a two-stage detector where the first stage,
# RPN, proposes candidate object bounding boxes, and the second stage uses these proposals to classify the object and refine the bounding box coordinates.

# keep in mind that the model's output labels are numerical. 
# If you want the actual category names (like "person", "dog", etc.),
# you would need a mapping from these numerical labels to the corresponding category names. 
# You can find this mapping in the COCO dataset's categories.

Popularity 1/10 Helpfulness 1/10 Language python

Source: Grepper

Tags: models object-detection python pytorch region

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Contributed on Jun 19 2023

Tablet50

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Two-stage object detection models with Pytorch usually consist of a region proposal network (RPN), implementing with faster R-CNN

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