fjuice

Yoooo 👋 welcome to fjuice.

We build vision systems, datasets, and models that don’t take themselves too seriously — but still run like production-grade tools.

Think:

• synthetic data pipelines that actually scale
• open-vocab vision models that don’t fall apart in the wild
• APIs that feel like: fjuice.detect() instead of corporate SDK soup

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🍊 What is fjuice?

fjuice is a research + engineering project focused on:

• synthetic dataset generation at scale (Juicebox series)
• open-vocab vision models (JuiceJet series)
• segmentation + grounding systems
• messy-real-world robustness via controlled randomness

We like structured chaos.

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🧃 Ecosystem

🧱 Juicebox

Synthetic datasets for vision training.

• multi-million to multi-billion scale generation
• heavy augmentation + realism injection
• segmentation-first design
• open vocab friendly

✈️ JuiceJet

Vision models trained on Juicebox datasets.

• segmentation / detection / grounding models
• open-vocab reasoning over visual scenes
• designed for robustness, not just benchmarks

⚙️ fjuice core

A lightweight API layer for running models:

fjuice.detect(image)
fjuice.segment(image)
fjuice.infer(prompt, image)

Yes, it’s intentionally simple.

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🧠 Philosophy

We don’t optimize for:

• fancy corporate frameworks
• over-engineered abstractions
• benchmark-only performance

We optimize for:

• real-world robustness
• dataset diversity
• reproducible chaos
• fast iteration

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🚀 Why it exists

Because most vision datasets are either:

• too clean
• too small
• too static
• or too boring

So we built something messy enough to generalize.

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🧪 Status

Actively evolving.

Stuff will break. Stuff will change. That’s the point.

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🪪 License

See LICENSE file.

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🌐 Links

• Hugging Face Org: (Visit Here!)
• Models: JuiceJet series
• Datasets: Juicebox series
• CONTRIBUTING: https://tally.so/r/VLN9zN

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Made with chaos, caffeine, and too many GPUs.