TorchSat is an open-source deep learning framework for satellite imagery analysis based on PyTorch.
This project is still work in progress. If you want to know the latest progress, please check the develop branch.
Hightlight
😉 Support multi-channels(> 3 channels, e.g. 8 channels) images and TIFF file as input.
😋 Convenient data augmentation method for classification, sementic segmentation and object detection.
😍 Lots of models for satellite vision tasks, such as ResNet, DenseNet, UNet, PSPNet, SSD, FasterRCNN ...
😃 Lots of common satellite datasets loader.
😮 Training script for common satellite vision tasks.
Install

source: python3 setup.py install
How to use

Introduction
Image Classification Tutorial: Docs, Google Colab
Semantic Segmentation Tutorial: Docs
Data Augumentation: Docs, Google Colab
Features

Data augmentation

We suppose all the input images, masks and bbox should be NumPy ndarray. The data shape should be [height, width] or [height, width, channels].
pixel level

Pixel-level transforms only change the input image and will leave any additional targets such as masks, bounding boxes unchanged. It support all channel images. Some transforms only support specific input channles.
Transform	Image	masks	BBoxes
ToTensor	✓	✓	✓
Normalize	✓	✓	✓
ToGray	✓	✓	✓
GaussianBlur	✓	✓	✓
RandomNoise	✓	✓	✓
RandomBrightness	✓	✓	✓
RandomContrast	✓	✓	✓
spatial-level

Spatial-level transforms will simultaneously change both an input image as well as additional targets such as masks, bounding boxes. It support all channel images.
Transform	Image	masks	BBoxes
Resize	✓	✓	✓
Pad	✓	✓	✓
RandomHorizontalFlip	✓	✓	✓
RandomVerticalFlip	✓	✓	✓
RandomFlip	✓	✓	✓
CenterCrop	✓	✓	✓
RandomCrop	✓	✓	✓
RandomResizedCrop	✓	✓	✓
ElasticTransform	✓	✓
RandomRotation	✓	✓	✓
RandomShift	✓	✓	✓
Models

Classification

All models support multi-channels as input (e.g. 8 channels).
VGG: vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19_bn, vgg19
ResNet: resnet18, resnet34, resnet50, resnet101, resnet152, resnext50_32x4d,resnext101_32x8d, wide_resnet50_2, wide_resnet101_2
DenseNet: densenet121, densenet169, densenet201
Inception: inception_v3
MobileNet: mobilenet_v2
EfficientNet: efficientnet_b0, efficientnet_b1, efficientnet_b2, efficientnet_b3,efficientnet_b4, efficientnet_b5, efficientnet_b6, efficientnet_b7
ResNeSt: resnest50, resnest101, resnest200, resnest269
Sementic Segmentation

UNet: unet, unet34, unet101, unet152 (with resnet as backbone.)
Dataloader

Classification

Showcase

If you extend this repository or build projects that use it, we'd love to hear from you.
Reference

torchvision
Note

If you are looking for the torchvision-enhance, please checkout the enhance branch. But it was deprecated.