inference-models
The new inference engine for Computer Vision models
Decision gist · record as of 2026-08-14
Yes, if you are building computer vision applications on Roboflow models or need a unified inference API across multiple backends and model types. The heavy dependency footprint and Python version cap (3.10–3.12) make it unsuitable for lightweight or resource-constrained environments. License treatment is unclear—verify model-specific restrictions before production deployment. No known vulnerabilities as of the query date.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10–3.12.
- Heavy dependencies (torch, torchvision, transformers) demand significant disk and memory; GPU optional but recommended for speed.
- Low install friction with a pure-Python wheel.
License · maintenance · safety
(unclear) — License treatment is unclear in the metadata; the description states Apache 2.0 for the package itself but notes individual models may have different licenses. Verify the actual license terms and model-specific restrictions before production use.
last release 2026-08-14 (0 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 93,972 downloads/mo, #13,353 on PyPI
Alternatives
Verify before relying
pip install inference-models
from inference_models import AutoModel
model = AutoModel.from_pretrained("rfdetr-base")
predictions = model(image)- Whether the Apache 2.0 license applies uniformly to all bundled model weights and architectures, or if model-specific restrictions apply at runtime.
- Performance characteristics and latency benchmarks across the supported backends (PyTorch, ONNX, TensorRT, Hugging Face).
- Whether the 44 runtime dependencies can be selectively installed via extras, as the description mentions a 'composable extras system' but the fact sheet does not detail which extras are available.
What it is and what it does
inference-models is a unified inference library for running computer vision models trained on Roboflow or built locally. It abstracts away backend selection (PyTorch, ONNX, TensorRT, Hugging Face) and model loading, letting you call a single API regardless of the underlying architecture—whether you're running object detection, segmentation, OCR, or vision-language tasks. The library includes pretrained models (RFDetr, SAM, Florence, DocTR, EasyOCR, YOLO, and others) and supports custom models from the Roboflow platform via API key, as well as local model implementations.
The package is designed for production use and integrates with supervision for annotation and post-processing. It requires Python 3.10–3.12 and pulls in 44 runtime dependencies including numpy, torch, torchvision, transformers, and diffusers, making it best suited for environments with adequate compute and storage. The library reached its first stable release at version 0.19.0 and is actively maintained, though the API may still evolve.
Use it for
- Load and run a pretrained Roboflow model (e.g., RFDetr) on images without writing backend-specific code.
- Deploy a custom model trained on Roboflow platform in production by loading it with an API key.
- Run local custom model implementations from a directory for production deployment of non-standard architectures.
- Chain inference with supervision to visualize predictions (bounding boxes, masks, etc.) on images.
- Switch between inference backends (PyTorch to TensorRT, for example) without changing application code.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building computer vision applications on Roboflow models or need a unified inference API across multiple backends and model types.
The heavy dependency footprint and Python version cap (3.10–3.12) make it unsuitable for lightweight or resource-constrained environments. License treatment is unclear—verify model-specific restrictions before production deployment. No known vulnerabilities as of the query date.
Install
inference-models on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance as of release date. Requires Python 3.10–3.12 and brings in 44 runtime dependencies including numpy, torch, torchvision, transformers, and diffusers; this is a heavy stack suitable for systems with compute capacity.
Requires Python 3.10–3.12. Heavy dependencies (torch, torchvision, transformers) demand significant disk and memory; GPU optional but recommended for speed.
License in practice
License treatment is unclear in the metadata; the description states Apache 2.0 for the package itself but notes individual models may have different licenses. Verify the actual license terms and model-specific restrictions before production use.
Quickstart
pip install inference-models
from inference_models import AutoModel
model = AutoModel.from_pretrained("rfdetr-base")
predictions = model(image)Verify before relying
- Whether the Apache 2.0 license applies uniformly to all bundled model weights and architectures, or if model-specific restrictions apply at runtime.
- Performance characteristics and latency benchmarks across the supported backends (PyTorch, ONNX, TensorRT, Hugging Face).
- Whether the 44 runtime dependencies can be selectively installed via extras, as the description mentions a 'composable extras system' but the fact sheet does not detail which extras are available.
Package facts
| License | Not declared unclear |
| Python support | Capped below the current Python release <3.13,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 44 packagesnumpytorchtorchvisionopencv-pythonrequestssupervisionbackoffpython-dotenvtransformersdiffuserstimmaccelerateeinopspeftnum2wordsbitsandbytespyvipsrf-clippython-doctrpackagingrichpydanticfilelocksegmentation-models-pytorchscikit-imageeasyocrsentencepiecerf_groundingdinotldextractpybase64 |
| Maintenance | Actively maintained 0 days since the last release |
| First released | |
| Downloads | 93,972 / month, #13,353 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: inference_models-0.35.2-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “roboflow model loading”
- inference-modelsLoads and runs computer vision models from Roboflow or local sources…
- roboflowRoboflow is a Python client for the Roboflow computer vision…
- inference-cliA command-line tool for running computer vision inference locally via…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also controlnet-aux · inference-cli · polygraphy · inference-sdk · roboflow · yolov5 · datarobot-predict · supervision · sit4onnx · tensorrt-cu13-libs