--- id: icevision version: "0.12.0" license: Apache-2.0 license_treatment: permissive maintenance: abandoned --- # icevision — Agnostic Computer Vision Framework License: permissive · Maintenance: abandoned · Downloads: 473.8K/mo ## What it is and what it does IceVision is an object detection framework that abstracts away model and training backend differences, letting you work with hundreds of pre-trained models from torchvision, MMLabs, and other sources through a single API. It handles the full pipeline—data curation, augmentation, model selection, and training orchestration—using libraries like PyTorch Lightning and Fastai as pluggable backends. The package includes data cleaning tools, an exploratory dashboard, pluggable transforms for augmentation, and support for multi-task learning combining detection, segmentation, and classification. However, the project has been abandoned since February 2022 and receives no active maintenance. It requires Linux or macOS and depends on a large stack of computer vision libraries (torch, torchvision, opencv-python, albumentations, and specialized detection models). Use it for: - Train object detection models on custom datasets using pre-trained weights from a curated model zoo without writing boilerplate training code. - Explore and clean image datasets using the built-in data curation and auto-fix tools before training. - Combine object detection with image segmentation or classification in a single multi-task training pipeline. - Experiment with different neural network architectures and training backends (PyTorch Lightning, Fastai) on the same dataset. - Leverage hundreds of pre-trained models from torchvision and MMLabs for transfer learning on detection tasks. ## Worth the install? AI-flagged interpretation of the facts above — verify before relying. IceVision provides a unified framework for training and deploying object detection models, supporting multiple model architectures and training backends like PyTorch Lightning and Fastai. No. The package is abandoned (no updates since February 2022) and depends on a large, rapidly evolving stack of computer vision libraries that may have become incompatible. For new projects, use actively maintained alternatives like Ultralytics YOLOv5/v8, Detectron2, or MMDetection. Install only if you are maintaining legacy code already using IceVision and cannot migrate. ## Install pip install icevision uv add icevision poetry add icevision ## Installing icevision Before you install: Low install friction, but the package is abandoned as of 1646 days since its last release. Depends on 15 runtime packages including torch, torchvision, and several specialized computer vision libraries, which may require system-level dependencies or significant disk space. License in practice: Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions—suitable for most projects that can accept the maintenance risk. Quickstart: pip install icevision[all] from icevision.all import * # Load a pre-trained model and prepare data for training Linux/MacOS only; requires PyTorch and torchvision installed; abandoned project with no active maintenance since 2022-02-10. Verify before relying: - Whether the package's dependencies (torch, torchvision, yolov5-icevision, effdet, sahi, resnest) remain compatible with current Python and PyTorch versions. - Whether the model zoo and pre-trained weights referenced in the description are still accessible and functional. - Whether the exploratory data analysis dashboard and auto-fix features work as documented. ## Package facts - License: Apache-2.0 (permissive) - Python support: supports_current - Install friction: low - Maintenance: abandoned - Downloads: 473.8K/month (top 15,000 on PyPI) - Known vulnerabilities: none known ## Tags object detection framework, computer vision model training, pytorch object detection, multi-model detection pipeline, neural network model zoo, image recognition training, detection model orchestration, object-detection, abandoned, pytorch [View on SkillFed](https://skillfed.io/packages/icevision) · [View on PyPI](https://pypi.org/project/icevision/)