hyperview 0.1.1

Open-source dataset curation with hyperbolic embeddings visualization

HyperView

Open-source dataset curation + embedding visualization (Euclidean + Poincaré disk)

Watch the Demo Video

---

Features

• Dual-Panel UI: Image grid + scatter plot with bidirectional selection
• Euclidean/Poincaré Toggle: Switch between standard 2D UMAP and Poincaré disk visualization
• HuggingFace Integration: Load datasets directly from HuggingFace Hub
• Fast Embeddings: Uses EmbedAnything for CLIP-based image embeddings

Quick Start

Docs: docs/datasets.md · docs/colab.md · CONTRIBUTING.md · TESTS.md

Installation

git clone https://github.com/Hyper3Labs/HyperView.git
cd HyperView

# Install with uv
uv venv .venv
source .venv/bin/activate
uv pip install -e ".[dev]"

Run the Demo

hyperview demo --samples 500

This will:

1. Load 500 samples from CIFAR-100
2. Compute CLIP embeddings
3. Generate Euclidean and Poincaré visualizations
4. Start the server at http://127.0.0.1:6262

Python API

import hyperview as hv

# Create dataset
dataset = hv.Dataset("my_dataset")

# Load from HuggingFace
dataset.add_from_huggingface(
    "uoft-cs/cifar100",
    split="train",
    max_samples=1000
)

# Or load from local directory
# dataset.add_images_dir("/path/to/images", label_from_folder=True)

# Compute embeddings and visualization
dataset.compute_embeddings(model="openai/clip-vit-base-patch32")
dataset.compute_visualization()

# Launch the UI
hv.launch(dataset)  # Opens http://127.0.0.1:6262

Google Colab

See docs/colab.md for a fast Colab smoke test and notebook-friendly launch behavior.

Save and Load Datasets

# Save dataset with embeddings
dataset.save("my_dataset.json")

# Load later
dataset = hv.Dataset.load("my_dataset.json")
hv.launch(dataset)

Why Hyperbolic?

Traditional Euclidean embeddings struggle with hierarchical data. In Euclidean space, volume grows polynomially ($r^d$), causing Representation Collapse where minority classes get crushed together.

Hyperbolic space (Poincaré disk) has exponential volume growth ($e^r$), naturally preserving hierarchical structure and keeping rare classes distinct.

Contributing

Development setup, frontend hot-reload, and backend API notes live in CONTRIBUTING.md.

Related projects

• hyper-scatter: High-performance WebGL scatterplot engine (Euclidean + Poincaré) used by the frontend: https://github.com/Hyper3Labs/hyper-scatter
• hyper-models: Non-Euclidean model zoo + ONNX exports (e.g. for hyperbolic VLM experiments): https://github.com/Hyper3Labs/hyper-models

References

• Poincaré Embeddings for Learning Hierarchical Representations (Nickel & Kiela, 2017)
• Hyperbolic Neural Networks (Ganea et al., 2018)

License

MIT License - see LICENSE for details.