Skip to content

The remote sensing image semantic segmentation repository based on tf.keras includes backbone networks such as resnet, densenet, mobilenet, and segmentation networks such as deeplabv3+, pspnet, panet, and refinenet.

Notifications You must be signed in to change notification settings

TachibanaYoshino/Remote-sensing-image-semantic-segmentation-tf2

Repository files navigation

Remote-sensing-image-semantic-segmentation-tf2

The remote sensing image semantic segmentation repository based on tf.keras includes backbone networks such as resnet, densenet, mobilenet, and segmentation networks such as deeplabv3+, pspnet, panet, and segnet.

This repository has been used to participate in the remote sensing semantic image segmentation track of the 2020 National Artificial Intelligence Competition (NAIC).


Data description

class label
Water 100
Transportation 200
Building 300
Arable land 400
Grassland 500
Woodland 600
Bare soil 700
Others 800

Requirements

  • python 3.7
  • tensorflow-gpu 2.3
  • opencv-python
  • tqdm
  • numpy
  • argparse
  • matplotlib
  • Pillow

Usage

1. Download dataset

data

2. Separate the validation set from the training data (optional)

python split_val_data_from_train.py

3. Complete the basic configuration of training and testing, such as data path, model path, etc.

Modify the config.py file

4. Train

python train.py --model DeepLabV3Plus --backBone ResNet152 --lr_scheduler cosine_decay --lr_warmup True

4. Download pre-trained weights

Link (TBD)

5. Inference

python predict.py

Results

MIou, FWIou
The inference result image is in the results folder

About

The remote sensing image semantic segmentation repository based on tf.keras includes backbone networks such as resnet, densenet, mobilenet, and segmentation networks such as deeplabv3+, pspnet, panet, and refinenet.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages