Object detection algorithm based on transformer model - BETR model

When the transformer model was first released, it was mainly used to deal with tasks in the NLP field, such as machine translation. However, with the popularity of the attention mechanism model, many magical reform models based on the transformer model have also been released one after another, and the attention mechanism of the transformer model has also been released. The Google team proved that it can be used in computer vision tasks, especially the release of the swin transformer model, which brought the transformer model into the field of computer vision. In the previous articles, we also introduced another model BETR based on the transformer model and applied to computer vision tasks. And the BETR model can be used not only for object detection, but also for object segmentation. In this issue, we will implement the BETR model based on the transformer model.

from PIL import Image
import requests
import matplotlib.pyplot as plt

import torch
from torch import nn
from torchvision.models import resnet50
import torchvision.transforms as T
torch.set_grad_enabled(False);

The first step in the code implementation of the object detection algorithm based on the transformer model is that we need to import the third-party library of python, here is mainly torch, make sure you are running this