{"id":43671526,"url":"https://github.com/imageomics/emb-explorer","last_synced_at":"2026-03-03T23:04:05.447Z","repository":{"id":335121534,"uuid":"1001126795","full_name":"Imageomics/emb-explorer","owner":"Imageomics","description":"An interactive tool for classifying images with a pretrained model and exploring clustering results in 2D space.","archived":false,"fork":false,"pushed_at":"2026-03-03T21:03:08.000Z","size":5971,"stargazers_count":1,"open_issues_count":1,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-03-03T21:08:58.718Z","etag":null,"topics":["clustering","data-visualization","dimensionality-reduction","eda","embedding","exploratory-data-analysis","exploratory-data-visualizations","image-exploration"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Imageomics.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":".zenodo.json","notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-06-12T21:38:51.000Z","updated_at":"2026-03-02T20:53:32.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/Imageomics/emb-explorer","commit_stats":null,"previous_names":["imageomics/emb-explorer"],"tags_count":2,"template":false,"template_full_name":null,"purl":"pkg:github/Imageomics/emb-explorer","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Imageomics%2Femb-explorer","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Imageomics%2Femb-explorer/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Imageomics%2Femb-explorer/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Imageomics%2Femb-explorer/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Imageomics","download_url":"https://codeload.github.com/Imageomics/emb-explorer/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Imageomics%2Femb-explorer/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30064798,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-03T18:21:05.932Z","status":"ssl_error","status_checked_at":"2026-03-03T18:20:59.341Z","response_time":61,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["clustering","data-visualization","dimensionality-reduction","eda","embedding","exploratory-data-analysis","exploratory-data-visualizations","image-exploration"],"created_at":"2026-02-05T00:09:23.743Z","updated_at":"2026-03-03T23:04:05.443Z","avatar_url":"https://github.com/Imageomics.png","language":"Python","readme":"# Image Embedding Explorer\n[![DOI](https://zenodo.org/badge/1001126795.svg)](https://doi.org/10.5281/zenodo.18841337)\n\nVisual 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.\n\n## Screenshots\n\n\u003ctable\u003e\n  \u003ctr\u003e\n    \u003ctd width=\"50%\" align=\"center\"\u003e\u003cb\u003eEmbed \u0026 Explore\u003c/b\u003e\u003c/td\u003e\n    \u003ctd width=\"50%\" align=\"center\"\u003e\u003cb\u003ePrecalculated Embedding Exploration\u003c/b\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e\u003cimg src=\"docs/images/app_screenshot_1.png\" alt=\"Embedding Interface\" width=\"100%\"\u003e\u003c/td\u003e\n    \u003ctd\u003e\u003cimg src=\"docs/images/app_screenshot_filter.png\" alt=\"Smart Filtering\" width=\"100%\"\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e\u003cimg src=\"docs/images/app_screenshot_2.png\" alt=\"Cluster Summary\" width=\"100%\"\u003e\u003c/td\u003e\n    \u003ctd\u003e\u003cimg src=\"docs/images/app_screenshot_cluster.png\" alt=\"Interactive Exploration\" width=\"100%\"\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e\u003c/td\u003e\n    \u003ctd\u003e\u003cimg src=\"docs/images/app_screenshot_taxon_tree.png\" alt=\"Taxonomy Tree\" width=\"100%\"\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\n## Features\n\n**Embed \u0026 Explore** - Embed images using pretrained models (CLIP, BioCLIP), cluster with K-Means, visualize with PCA/t-SNE/UMAP, and repartition images by cluster.\n\n**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.\n\n## Installation\n\n```bash\ngit clone https://github.com/Imageomics/emb-explorer.git\ncd emb-explorer\n\n# Using uv (recommended)\nuv venv \u0026\u0026 source .venv/bin/activate\nuv pip install -e .\n```\n\n### GPU Acceleration (optional)\n\nA 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/).\n\n```bash\n# CUDA 12.x \nuv pip install -e \".[gpu-cu12]\"\n\n# CUDA 13.x\nuv pip install -e \".[gpu-cu13]\"\n```\n\nThe 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.\n\n## Usage\n\n### Standalone Apps\n\n```bash\n# Embed \u0026 Explore - Interactive image embedding and clustering\nstreamlit run apps/embed_explore/app.py\n\n# Precalculated Embeddings - Explore precomputed embeddings from parquet\nstreamlit run apps/precalculated/app.py\n```\n\n### Entry Points (after pip install)\n\n```bash\nemb-embed-explore    # Launch Embed \u0026 Explore app\nemb-precalculated    # Launch Precalculated Embeddings app\nlist-models          # List available embedding models\n```\n\n### Example Data\n\nAn 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.\n\n### Remote HPC Usage\n\n```bash\n# On compute node\nstreamlit run apps/precalculated/app.py --server.port 8501\n\n# On local machine (port forwarding)\nssh -N -L 8501:\u003cCOMPUTE_NODE\u003e:8501 \u003cUSER\u003e@\u003cLOGIN_NODE\u003e\n\n# Access at http://localhost:8501\n```\n\n## Acknowledgements\n\n[OpenCLIP](https://github.com/mlfoundations/open_clip) | [Streamlit](https://streamlit.io/) | [Altair](https://altair-viz.github.io/)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fimageomics%2Femb-explorer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fimageomics%2Femb-explorer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fimageomics%2Femb-explorer/lists"}