---
id: autogluon-vision
version: "0.6.2"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# autogluon.vision — AutoML for Image, Text, and Tabular Data

License: permissive · Maintenance: active · Downloads: 86.2K/mo

## What it is and what it does

AutoGluon Vision is an AutoML toolkit that automates the machine learning pipeline for image classification and object detection tasks. It abstracts away model selection, hyperparameter tuning, and training orchestration, allowing you to achieve competitive results with just a few lines of code. The package wraps deep learning frameworks and integrates with AutoGluon's core ensemble and multimodal infrastructure to handle the full workflow from raw images to predictions.

The package is designed for practitioners who want strong predictive performance without manual architecture search or extensive hyperparameter experimentation. It supports both image classification and object detection, and is part of the broader AutoGluon ecosystem. The dependency chain is substantial—requiring numpy, pandas, Pillow, matplotlib, and multiple AutoGluon submodules—so installation and environment setup are non-trivial.

Use it for:

- Rapid prototyping of image classification models for Kaggle competitions or proof-of-concept projects without manual tuning.
- Automated object detection on custom datasets where you want competitive accuracy without deep learning expertise.
- Benchmarking baseline performance on image tasks before investing in custom architecture design.
- Production image inference pipelines where AutoML-selected models provide good accuracy-latency tradeoffs.
- Multi-task learning combining images with tabular or text data via autogluon.multimodal integration.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Automated machine learning for image classification and object detection, handling model selection, hyperparameter tuning, and training with minimal code.

Yes, with conditions. Install if you need rapid AutoML for image classification or object detection and can tolerate the substantial dependency footprint and Python version cap (3.7–3.9). The package is actively maintained and permissively licensed. However, the latest release is from 2023-01-11, so verify ongoing support and compatibility with your target environment before committing to production use.

## Install

pip install autogluon-vision
uv add autogluon-vision
poetry add autogluon-vision

## Installing autogluon.vision

Before you install: Low friction installation via wheel distribution. Actively maintained with recent commits and a large repository presence (10596 stars). Requires 8 runtime dependencies including numpy, pandas, and gluoncv, which may add setup complexity for new environments.

License in practice: Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions. Suitable for proprietary and open-source projects.

Quickstart:

pip install autogluon.vision

from autogluon.vision import ImagePredictor
predictor = ImagePredictor().fit(train_data, time_limit=120)
results = predictor.predict(test_data)

Requires Python >=3.7, <3.10. Substantial dependency chain (gluoncv, timm, matplotlib, autogluon.core, autogluon.multimodal) may require compatible environments.

Verify before relying:

- Whether autogluon.vision is actively maintained beyond the latest release date (2023-01-11); days since release warrants confirmation of ongoing support.
- Specific GPU/CUDA requirements and whether CPU-only inference is practical for production workloads.
- Memory footprint and typical training time for common image datasets to assess real-world feasibility.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: capped_below_current
- Install friction: low
- Maintenance: active
- Downloads: 86.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

automl image classification, automated deep learning vision, object detection automl, image recognition without tuning, neural architecture search images, end-to-end image prediction, computer vision automation, automl, computer-vision, deep-learning

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