https://github.com/imageomics/emb-explorer
An interactive tool for classifying images with a pretrained model and exploring clustering results in 2D space.
https://github.com/imageomics/emb-explorer
clustering data-visualization dimensionality-reduction eda embedding exploratory-data-analysis exploratory-data-visualizations image-exploration
Last synced: about 2 months ago
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An interactive tool for classifying images with a pretrained model and exploring clustering results in 2D space.
- Host: GitHub
- URL: https://github.com/imageomics/emb-explorer
- Owner: Imageomics
- License: mit
- Created: 2025-06-12T21:38:51.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2026-03-03T21:03:08.000Z (about 2 months ago)
- Last Synced: 2026-03-03T21:08:58.718Z (about 2 months ago)
- Topics: clustering, data-visualization, dimensionality-reduction, eda, embedding, exploratory-data-analysis, exploratory-data-visualizations, image-exploration
- Language: Python
- Homepage:
- Size: 5.69 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
- Zenodo: .zenodo.json
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README
# Image Embedding Explorer
[](https://doi.org/10.5281/zenodo.18841337)
Visual exploration and clustering tool for image embeddings. Users can either bring pre-calculated embeddings to explore, or use the interface to embed their images and then explore those embeddings.
## Screenshots
Embed & Explore
Precalculated Embedding Exploration
## Features
**Embed & Explore** - Embed images using pretrained models (CLIP, BioCLIP), cluster with K-Means, visualize with PCA/t-SNE/UMAP, and repartition images by cluster.
**Precalculated Embeddings** - Load parquet files (or directories of parquets) with precomputed embeddings, apply dynamic cascading filters, and explore clusters with taxonomy tree navigation. See [Data Format](docs/DATA_FORMAT.md) for the expected schema and [Backend Pipeline](docs/BACKEND_PIPELINE.md) for how embeddings flow through clustering and visualization.
## Installation
```bash
git clone https://github.com/Imageomics/emb-explorer.git
cd emb-explorer
# Using uv (recommended)
uv venv && source .venv/bin/activate
uv pip install -e .
```
### GPU Acceleration (optional)
A GPU is **not required** — everything works on CPU out of the box. But if you have an NVIDIA GPU with CUDA, clustering and dimensionality reduction (KMeans, t-SNE, UMAP) will be significantly faster via [cuML](https://docs.rapids.ai/api/cuml/stable/).
```bash
# CUDA 12.x
uv pip install -e ".[gpu-cu12]"
# CUDA 13.x
uv pip install -e ".[gpu-cu13]"
```
The app auto-detects GPU availability at runtime and falls back to CPU if anything goes wrong — no configuration needed. You can also manually select backends (cuML, FAISS, sklearn) in the sidebar.
## Usage
### Standalone Apps
```bash
# Embed & Explore - Interactive image embedding and clustering
streamlit run apps/embed_explore/app.py
# Precalculated Embeddings - Explore precomputed embeddings from parquet
streamlit run apps/precalculated/app.py
```
### Entry Points (after pip install)
```bash
emb-embed-explore # Launch Embed & Explore app
emb-precalculated # Launch Precalculated Embeddings app
list-models # List available embedding models
```
### Example Data
An example dataset (`data/example_1k.parquet`) is provided with BioCLIP 2 embeddings for testing. Please see the [data README](data/README.md) for more information about this sample set.
### Remote HPC Usage
```bash
# On compute node
streamlit run apps/precalculated/app.py --server.port 8501
# On local machine (port forwarding)
ssh -N -L 8501::8501 @
# Access at http://localhost:8501
```
## Acknowledgements
[OpenCLIP](https://github.com/mlfoundations/open_clip) | [Streamlit](https://streamlit.io/) | [Altair](https://altair-viz.github.io/)