perceptron
Perceptron multimodal SDK
Decision gist · record as of 2026-08-14
Yes, with conditions. Perceptron is actively maintained, has no known vulnerabilities, and low install friction. It is worth installing if you are building physical AI applications (robotics, manufacturing, security) and need a unified SDK for grounded perception. However, verify the license terms before production use—the metadata does not specify whether it is open-source, proprietary, or commercial. Also confirm that the model families and API access you need are included in your intended deployment model.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- API credentials (PERCEPTRON_API_KEY) needed for actual inference; SDK returns compile-only payloads when missing.
- Low friction: pure Python wheel with eight runtime dependencies (rich, typer, Pillow, numpy, httpx, shellingham, colorama, pydantic).
License · maintenance · safety
(unclear) — License status is unclear—no SPDX identifier or raw license text provided in metadata. Verify the actual license terms before deploying in production or redistributing.
last release 2026-05-12 (94 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 78,514 downloads/mo, #14,438 on PyPI
Alternatives
Verify before relying
pip install perceptron
from perceptron import detect, caption
result = detect("image.jpg", classes=["object"])
for box in result.points or []:
print(f"{box.mention}: {box.top_left}")
desc = caption("scene.png", style="detailed")
print(desc.text)- Whether license is proprietary, open-source, or commercial—metadata does not specify
- Whether Perceptron Mk1 and Isaac model families are included or require separate subscription
- Actual latency and cost characteristics for edge models (Isaac 0.1, Isaac 0.2 2B)
- Whether optional extras (torch, dev) are freely available or require additional dependencies
What it is and what it does
Perceptron is a Python SDK for physical AI—robotics, manufacturing, logistics, and security applications—that wraps access to grounded perception models (Perceptron Mk1 for cloud, Isaac family for edge). It provides a unified interface for detection with structured bounding boxes, image captioning, OCR, and visual Q&A, all with spatial grounding and in-context learning from annotated examples. The SDK abstracts provider selection (defaulting to Perceptron but supporting swappable backends), handles coordinate normalization and streaming responses, and includes a CLI for batch processing.
The package depends on rich, typer, Pillow, numpy, httpx, shellingham, colorama, and pydantic—a moderate but manageable set of dependencies. It targets modern Python (3.10+) and is designed for both cloud workloads (where capability matters) and edge deployment (where latency and footprint are critical). Configuration is flexible: environment variables, code-level context managers, or CLI commands. When API credentials are absent, the SDK returns compile-only payloads so you can inspect requests before sending them.
Use it for
- Detect defects or safety violations in manufacturing images with few-shot learning from annotated examples.
- Build robotics pipelines that localize objects and obstacles with grounded spatial reasoning across video frames.
- Extract text and structured data from documents or schematics using OCR with custom prompts.
- Run real-time perception on edge devices (e.g., Isaac 0.2 2B) with low latency and minimal serving cost.
- Compose multimodal workflows combining detection, captioning, and Q&A using the typed DSL.
- Batch-process image directories with the CLI to generate JSON summaries of detections or captions.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
Perceptron is actively maintained, has no known vulnerabilities, and low install friction. It is worth installing if you are building physical AI applications (robotics, manufacturing, security) and need a unified SDK for grounded perception. However, verify the license terms before production use—the metadata does not specify whether it is open-source, proprietary, or commercial. Also confirm that the model families and API access you need are included in your intended deployment model.
Install
perceptron on PyPI
Before you install
Low friction: pure Python wheel with eight runtime dependencies (rich, typer, Pillow, numpy, httpx, shellingham, colorama, pydantic). Active maintenance since first release in September 2025, last updated May 2026. No known vulnerabilities.
Requires Python 3.10 or later. API credentials (PERCEPTRON_API_KEY) needed for actual inference; SDK returns compile-only payloads when missing.
License in practice
License status is unclear—no SPDX identifier or raw license text provided in metadata. Verify the actual license terms before deploying in production or redistributing.
Quickstart
pip install perceptron
from perceptron import detect, caption
result = detect("image.jpg", classes=["object"])
for box in result.points or []:
print(f"{box.mention}: {box.top_left}")
desc = caption("scene.png", style="detailed")
print(desc.text)Verify before relying
- Whether license is proprietary, open-source, or commercial—metadata does not specify
- Whether Perceptron Mk1 and Isaac model families are included or require separate subscription
- Actual latency and cost characteristics for edge models (Isaac 0.1, Isaac 0.2 2B)
- Whether optional extras (torch, dev) are freely available or require additional dependencies
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagesrichtyperPillownumpyhttpxshellinghamcoloramapydantic |
| Maintenance | Actively maintained 94 days since the last release |
| First released | |
| Downloads | 78,514 / month, #14,438 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: perceptron-0.3.5-py3-none-any.whl
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