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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Satellite Image Classification using semantic segmentation methods in deep learning
| Date | Stars |
|---|---|
| 2026-07-24 | 320 |
| 2026-07-25 | 320 |
| 2026-07-28 | 320 |
| 2026-07-30 | 320 |
| 2026-07-31 | 320 |
| 2026-08-06 | 320 |
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# Eye In The Sky [Satellite Image Classification](http://inter-iit.tech/events/the-eye-in-the-sky.html), InterIIT Techmeet 2018, IIT Bombay. Team: [Manideep Kolla](https://github.com/manideep2510), [Aniket Mandle](https://github.com/aniketmandle-sopho), [Apoorva Kumar](https://github.com/cybr17crwlr) ## About This repository contains the implementation of two algorithms namely [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/pdf/1505.04597.pdf) and [Pyramid Scene Parsing Network](https://arxiv.org/pdf/1612.01105.pdf) modified for the problem of satellite image classification. ## Files - [`main_unet.py`](main_unet.py) : Python code for training the algorithm with U-Net architecture including the encoding of the ground truths. - [`unet.py`](unet.py) : Contains our implementation of U-Net layers. - [`test_unet.py`](test_unet.py) : Code for Testing, calculating accuracies, calculating confusion matrices for training and validation and saving predictions by the U-Net model on training, validation and testing images. - [`Inter-IIT-CSRE`](Inter-IIT-CSRE) : Contains all the training, validation ad testing data. - [`Comparison_Test.pdf`](Comparison_Test.pdf) : Side by side comparison of the test data with the U-Net model predictions on the data. - [`train_predictions`](train_predictions) : U-Net Model predictions on training and validation images. - [`plots`](plots) : Accuracy and loss plots for training and validation for U-Net architecture. - [`Test_images`](Test_images), [`Test_outputs`](Test_outputs) : Contains test images and their predictions b the U-Net model. - [`class_masks`](class_masks), [`compare_pred_to_gt`](compare_pred_to_gt), [`images_for_doc`](images_for_doc) : Contains several images for documentation. - [`PSPNet`](PSPNet) : Contains training files for implementation of PSPNet algorithm to satellite image classification. ## Usage Clone the repository, change your present working directory to the cloned directory. Create folders with names `train_predictions` and `test_outputs` to save model predicted outputs on training and testing images (Not required now as the repo already contains these folders) ``` $ git clone https://github.com/manideep2510/eye-in-the-sky.git $ cd eye-in-the-sky $ mkdir train_predictions $ mkdir test_outputs ``` For training the U-Net model and saving weights, run the below command ``` $ python3 main_unet.py ``` To test the U-Net model, calculating accuracies, calculating confusion matrices for training and validation and saving predictions by the model on training, validation and testing images. ``` $ python3 test_unet.py ``` ## Note : You might get an error `xrange is not defined` while running our code. This error is not due to errors in our code but due to not up to date python package named `libtiff` (some parts of the source code of the package are in python2 and some are in python3) which we used to read the dataset which in which the images are in .tif format. We were not able to use other libraries like openCV or PIL to read the images as they are not properly supporting to read the 4-channel .tif images. This error can be resolved by editing the source code of the `libtiff` library. Go to the file in the source code of the library from where the error arises (the file name will be displayed in the terminal when it is showing the error) and replace all the ```xrange()``` (python2) functions in the file to ```range()``` (python3). ## Pre-trained Model We are providing some reasonably good pre-trained weights here so that the users don't need to train from scratch. | Description | Task | Dataset | Model | | ------------- | ----------------- | ------------------- | ------------------------------------------------------------ | | UNet Architecture | Satellite Image Classification | IITB dataset (Refer [`Inter-IIT-CSRE`](Inter-IIT-CSRE) folder) | [downloa
Excerpt of 15,259 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:67313ae5d4a1a3b6, topic:deep-learning, topic:tensorflow, readme:train from scratch
matched fp:67313ae5d4a1a3b6, topic:computer-vision, readme:image segmentation, desc:semantic segmentation