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Pytorch code for the implementation of object detection at on stage model

Tablet50 answered on June 18, 2023 Popularity 5/10 Helpfulness 2/10

answer Pytorch code for the implementation of object detection at on stage model

Pytorch code for the implementation of object detection at on stage model

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# Simple One stage object detection model templet architecture

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")

# NOte 
# Pytorch - torchvision library used to implement re-trained models for object detection, using the Fast R-CNN model.
# This script loads an image from the provided path, applies the necessary transformations,
# and then feeds the image through the model to get the prediction.
# The function will then return a list of bounding boxes and labels for detections with a confidence score higher than 0.5.

# Runing the code: You have to download the image for testing and replace the "image.jpg" with your image path.
# Also, you need to have torchvision installed in your Python environment. If not, you can install it via pip:

# Also, 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.

# This is a very basic example, 
# Real-world use-cases can be much more complex and might involve additional preprocessing/postprocessing steps,
# and handling of different input sizes.

Popularity 5/10 Helpfulness 2/10 Language python

Source: Grepper

Tags: implementation model object-detection python pytorch stage

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Contributed on Jun 18 2023

Tablet50

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Pytorch code for the implementation of object detection at on stage model

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