A modular, dataset-agnostic and model-agnostic framework for phenotyping analysis, combining deep learning embeddings and image properties.
🗺️ Navigation
✨ Features | 🚀 Quick Start | 📦 Installation | 📖 Documentation | 📜 Citation
✨ Features
- 🧠 Model Agnostic: Extract embeddings using DINOv2 or any custom vision model (PyTorch, TensorFlow, Keras, etc.) via the modular
ModelWrapperinterface. - 📂 Dataset Agnostic: Works with any image dataset structure with customizable metadata extraction.
- 🛠️ Analysis Toolkit: Distance quantification, anomaly detection, property extraction, and correlation analysis.
- 📊 Interactive Visualizations: High-performance Plotly-based PCA, t-SNE, and UMAP plots.
- 📑 Automated Reporting: Generate comprehensive standalone HTML reports.
🚀 Quick Start
Try the no-code experience with the Interactive Quickstart Notebook or launch instantly in Colab:
📦 Installation
pip install phenome
PyTorch is included. An NVIDIA GPU is used when CUDA is available; the same install runs on CPU.
Conda or from source
Clone the repository, then create one environment for your hardware. These files pin the scientific stack and install this checkout.
git clone https://github.com/AAitorG/PhenoMe.git
cd PhenoMe
GPU
conda env create -f envs/environment-gpu.yml
conda activate phenome-gpu
CPU
conda env create -f envs/environment-cpu.yml
conda activate phenome-cpu
💡 Full instructions, verification, and troubleshooting: See the Getting started guide for install details, verification commands, and first-analysis walkthroughs.
📖 Documentation
Explore the Full Documentation for detailed guides, API references, and tutorials.
| Step | Resource |
|---|---|
| 1️⃣ Install | Getting started |
| 2️⃣ Set up your data | Data setup · Experiment details |
| 3️⃣ Tutorials | Notebooks/tutorials/ |
| 4️⃣ Examples | Notebooks/examples/ |
Learn More: Core concepts · Workflows · FAQ · Glossary
📜 Citation
If you use PhenoMe in your research, please cite:
@software{phenome,
author = {Aitor González-Marfil},
title = {PhenoMe: Interactive AI framework for image representation analysis},
year = {2026},
url = {https://github.com/AAitorG/PhenoMe}
}
1 maintainer
Metadata
1 maintainer
Release files for phenome 1.4.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| phenome-1.4.1.tar.gz | 313.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| phenome-1.4.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 667.4 kB
Release files / phenome-1.4.1.tar.gz
| Download URL | phenome-1.4.1.tar.gz |
|---|---|
| Size | 313.1 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Yes |
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Transparency logRelease files / phenome-1.4.1-py3-none-any.whl
| Download URL | phenome-1.4.1-py3-none-any.whl |
|---|---|
| Size | 354.3 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 30, 2026.
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