本次代写是一个Python图像分类预测的assignment

IMAGE SEGMENTATION

In this assignment, you will implement a remote sensing segmentation algorithm using U-Nets.

Images are from the ISPRS Potsdam dataset1. Each input image in the dataset was divided into
image patches of size 300 × 300. These patches are divided into training (~2000 images) and
testing (~400 images) datasets (see the notebooks below) and labeled with one or more of the
following labels: {“roads”, “buildings”, “low veg”, “trees”, “cars”, “clutter”}. You will develop
a U-Net based system that predicts such labels.

You can access the data and ground truth labels from the following link

You are required to develop the following:

a) Build a U-Net neural network to solve this segmentation problem.

b) Train the neural network developed in part (a) using the training data. For faster training, you
should use GPU resources (you can get hours of free GPU time using Kaggle or Colab), and
for better results, shuffle the training data.

c) Apply the learned neural network to testing data and report the obtained accuracy. Avoid
underfitting or overfitting your data. A classification accuracy higher than 70% is
considered acceptable.

d) Display three sample results that include color images, the ground truth labels, and
predicted labels

The following notebooks can help you get started:

Kaggle (preferred) link, Colab link