Skip to content

Trained a ResNet50 model on the EuroSAT satellite imagery dataset w/ PyTorch. Analyzed the model's encoder by visualizing linear interpolations within the embedding space to illustrate the semantic separation in the learned feature representations.

License

Notifications You must be signed in to change notification settings

kushagraghosh/EuroSAT

Repository files navigation

Goals

Trained a ResNet50 model on the EuroSAT satellite imagery dataset w/ PyTorch. Analyzed the model's encoder by visualizing linear interpolations within the embedding space to illustrate the semantic separation in the learned feature representations.

Dataset

This project utilizes the EuroSAT dataset, which offers satellite images categorized into 10 distinct classes. The dataset is available for public access and can be found on the EuroSAT GitHub repository. I split the data into a training and testing set with an 80%-20% ratio.

The dataset is organized in the following directory hierarchy:

Train_Test_Splits
│
├── train
│   ├── AnnualCrop
│   ├── [Other Classes]
│
└── test
    ├── AnnualCrop
    ├── [Other Classes]

Make sure that the data path in the config.yaml is correct.

use pip to install all the requirements.

pip install -r requirements.txt

Linear Interpolation In Latent Space

Showing nearest neighbors at each interpolation step from left (Industrial) to right (Forest). Inspired by plot in page 7 of Tile2Vec paper.

Matplotlib plot

About

Trained a ResNet50 model on the EuroSAT satellite imagery dataset w/ PyTorch. Analyzed the model's encoder by visualizing linear interpolations within the embedding space to illustrate the semantic separation in the learned feature representations.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published