https://github.com/borda/kaggle_image-segm

Simple tooling for instance segmentation aiming at cell biology...
https://github.com/borda/kaggle_image-segm

cell-biology deep-learning image-recognition instance-segmentation kaggle

Last synced: 5 months ago
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Simple tooling for instance segmentation aiming at cell biology...

• Host: GitHub
• URL: https://github.com/borda/kaggle_image-segm
• Owner: Borda
• License: mit
• Created: 2021-10-16T18:24:03.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2025-01-06T17:13:11.000Z (9 months ago)
• Last Synced: 2025-03-31T08:07:05.148Z (6 months ago)
• Topics: cell-biology, deep-learning, image-recognition, instance-segmentation, kaggle
• Language: Jupyter Notebook
• Homepage:
• Size: 3.03 MB
• Stars: 10
• Watchers: 3
• Forks: 2
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Kaggle: Image Segmentation

[![CI complete testing](https://github.com/Borda/kaggle_image-segm/actions/workflows/ci_testing.yml/badge.svg?branch=main&event=push)](https://github.com/Borda/kaggle_image-segm/actions/workflows/ci_testing.yml)

[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/Borda/kaggle_image-segm/main.svg)](https://results.pre-commit.ci/latest/github/Borda/kaggle_image-segm/main)

[![codecov](https://codecov.io/gh/Borda/kaggle_image-segm/branch/main/graph/badge.svg)](https://codecov.io/gh/Borda/kaggle_image-segm)

[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/Borda/kaggle_image-segm/main.svg)](https://results.pre-commit.ci/latest/github/Borda/kaggle_image-segm/main)

### install this tooling

A simple way how to use this basic functions:

```bash

! pip install https://github.com/Borda/kaggle_image-segm/archive/refs/heads/main.zip

```

## Kaggle: [Tract Image Segmentation](https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation)

The goal of this challenge is to segment organs in medical scans to improve cancer treatment

![Sample organs visual](./assets/tract-annot.png)

### run notebooks in Kaggle

- [Tract🩻Segm: EDA 🔎 & 3D🗄️data browser](https://www.kaggle.com/code/jirkaborovec/tract-segm-eda-3d-data-browser)

- [Tract🩻Segm: Statistic⚖️predictions](https://www.kaggle.com/code/jirkaborovec/tract-segm-statistic-predictions)

- [Tract🩻Segm: EDA 🔎 & baseline Flash⚡DeepLab-v3 & albumentations](https://www.kaggle.com/code/jirkaborovec/tract-segm-eda-baseline-flash-deeplab-v3)

- [](<>)

### local notebooks

- [Tract segmentation with pure statistic](./notebooks/Tract-segm_statistic-predictions.ipynb)

- [Tract segmentation: EDA, baseline with Flash & DeepLab-v3](./notebooks/Tract-segm_EDA-baseline-Flash-DeepLab-v3.ipynb)

- [](<>)

### some results

Training progress with ResNext50 with training for 20 epochs > over 0.80 validation IoU:

![Training process](./assets/tract-segm_metrics.png)

## Kaggle: [Cell Instance Segmentation](https://www.kaggle.com/c/sartorius-cell-instance-segmentation)

The goal of this challenge is to detect cells in microscope images.

![Sample cells visual](./assets/cells-annot.png)

### run notebooks in Kaggle

- [🦠Cell Instance Segm: 🔍 interactive data browsing](https://www.kaggle.com/jirkaborovec/cell-instance-segm-interactive-data-browsing)

- [🦠Cell Instance Segm. ~ Lightning⚡Flash](https://www.kaggle.com/jirkaborovec/cell-instance-segm-lightning-flash)