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).

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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