metadata
title: Shape2Force
short_description: Force map prediction from bright-field cell images
emoji: 🦠
colorFrom: indigo
colorTo: blue
tags:
- cell-mechanobiology
- microscopy
- image-to-image
- pytorch
license: cc-by-4.0
sdk: docker
app_port: 8501
Shape2Force (S2F) App
Predict force maps from bright-field microscopy images using deep learning.
Quick Start
pip install -r requirements.txt
streamlit run app.py
Checkpoints are downloaded automatically from the Shape2Force model repo when running in Docker. For local use, place .pth files in ckp/.
Usage
- Choose Model type: Single cell or Spheroid
- Select a Checkpoint from
ckp/ - For single-cell: pick Substrate (e.g. fibroblasts_PDMS)
- Upload an image or pick from
samples/ - Click Run prediction
Output: heatmap, cell force (sum), and basic stats.
Full Project
For training, evaluation, and notebooks, see the main Shape2Force repository.