---
id: pretrainedmodels
version: "0.7.4"
license: unclear
license_treatment: permissive
maintenance: abandoned
---

# pretrainedmodels — Pretrained models for Pytorch

License: permissive · Maintenance: abandoned · Downloads: 85.1K/mo

## What it is and what it does

Pretrainedmodels is a PyTorch package that wraps pretrained convolutional neural network models trained on ImageNet. It provides a consistent API across many architectures—ResNet, DenseNet, Inception, NASNet, SENet, DualPathNet, Xception, and others—allowing you to load models with weights already initialized from large-scale training. The package exposes model attributes like input size, normalization statistics, and intermediate layer access for transfer learning and feature extraction.

The package was designed to help reproduce research results and enable transfer learning setups by offering a torchvision-like interface. However, it has been abandoned since late 2018 and receives no maintenance. While it may still work for loading older model checkpoints, it is not updated for modern PyTorch conventions or security practices, and model weight URLs may be stale.

Use it for:

- Load a pretrained ResNet or DenseNet for transfer learning on a custom image classification task without retraining from scratch.
- Extract intermediate features from a pretrained model for use in a downstream task like object detection or segmentation.
- Reproduce results from research papers published around 2017–2018 that relied on specific pretrained architectures and their exact weight initializations.
- Access multiple pretrained architectures through a single unified API rather than hunting for individual model implementations.

## Worth the install?

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

Provides a unified interface to load pretrained PyTorch convolutional neural networks (ResNet, DenseNet, Inception, NASNet, SENet, and others) with weights initialized from ImageNet training.

No. The package is abandoned, with no updates since 2018-10-29 and no commits since 2022-04-22. Modern PyTorch and torchvision have superseded it with actively maintained model zoos, better API design, and current weight URLs. Install only if you must reproduce a specific 2018-era result or need a model not available elsewhere; otherwise, use torchvision or timm instead.

## Install

pip install pretrainedmodels
uv add pretrainedmodels
poetry add pretrainedmodels

## Installing pretrainedmodels

Before you install: High install friction with no runtime dependencies listed but requiring manual setup. Maintenance is abandoned—last release was 2018-10-29, with no commits since 2022-04-22. Use only if you need models from that era and cannot migrate to actively maintained alternatives.

License in practice: Licensed under MIT (permissive), so you can use, modify, and distribute freely with minimal restrictions. No commercial or proprietary constraints.

Quickstart:

pip install pretrainedmodels
import pretrainedmodels
model = pretrainedmodels.resnet50(pretrained=True)
output = model(input_tensor)

Requires PyTorch to be installed separately; no explicit Python version requirement stated in the fact sheet.

Verify before relying:

- Whether the package still downloads model weights successfully given the age of the project and potential URL changes.
- Compatibility with modern PyTorch versions beyond what was tested in 2018.
- Whether all listed models remain fully functional without maintenance.
- Current status of model weight URLs and hosting infrastructure.

## Package facts

- License: not declared (permissive)
- Python support: unspecified
- Install friction: high
- Maintenance: abandoned
- Downloads: 85.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

pretrained pytorch models, imagenet weights pytorch, transfer learning models, resnet densenet inception pretrained, pytorch model zoo, convolutional neural network weights, pytorch feature extraction, transfer-learning, computer-vision, abandoned

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