efficientnet-pytorch
EfficientNet implemented in PyTorch.
Decision gist · record as of 2026-08-14
No, unless you are locked into an older PyTorch environment. The package is abandoned (last release April 2021, no commits since), so it will not receive security updates, compatibility fixes, or support for modern PyTorch versions. If you need EfficientNet models, consider using PyTorch's built-in torchvision.models or the official TensorFlow implementation instead.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PyTorch to be installed separately; no runtime dependencies listed in package metadata, so you must manage PyTorch installation yourself.
- Installation has high friction and the package is abandoned—last release was April 2021, nearly three years ago, with no commits since.
- While the repository is not archived, the lack of maintenance means security updates and compatibility fixes are unlikely.
License · maintenance · safety
Apache (permissive) — Licensed under Apache (permissive), so you may use and modify the code freely in commercial or private projects without restriction, though you must retain license notices.
last release 2021-04-15 (1947 days) · last repo commit 2022-04-08 · 8,222 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 162,491 downloads/mo, #10,599 on PyPI
Alternatives
Verify before relying
pip install efficientnet-pytorch
from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_pretrained('efficientnet-b0')
# For adversarial training variant:
model = EfficientNet.from_pretrained('efficientnet-b0', advprop=True)- Whether the package remains compatible with recent PyTorch versions (last tested in 2021).
- Whether pretrained weights are still accessible from the original sources.
- Whether EfficientNetV2 implementation mentioned in April 2021 update was ever completed and released.
What it is and what it does
EfficientNet PyTorch is a PyTorch reimplementation of the EfficientNet family of convolutional neural networks, which are designed to achieve high image classification accuracy with fewer parameters and lower computational cost than previous models. The package provides pretrained models (EfficientNet-B0 through B8) that can be loaded directly for inference or adapted for transfer learning on custom datasets.
The package supports both standard ImageNet-pretrained weights and adversarially-trained (advprop) variants, with different preprocessing requirements for each. You can use models for classification, feature extraction, or export to ONNX format. However, the package has been abandoned since April 2021 and receives no maintenance, meaning it may not work reliably with modern PyTorch versions or Python environments.
Use it for
- Load a pretrained EfficientNet model for image classification on new images without retraining.
- Extract learned features from intermediate layers for use in downstream tasks or custom classifiers.
- Fine-tune an EfficientNet on a custom dataset by loading a pretrained model with a different number of output classes.
- Benchmark or compare EfficientNet accuracy against other architectures on ImageNet or custom validation sets.
- Export a trained EfficientNet to ONNX format for deployment in production environments outside PyTorch.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No, unless you are locked into an older PyTorch environment.
The package is abandoned (last release April 2021, no commits since), so it will not receive security updates, compatibility fixes, or support for modern PyTorch versions. If you need EfficientNet models, consider using PyTorch's built-in torchvision.models or the official TensorFlow implementation instead.
Install
efficientnet-pytorch on PyPI
Before you install
Installation has high friction and the package is abandoned—last release was April 2021, nearly three years ago, with no commits since. While the repository is not archived, the lack of maintenance means security updates and compatibility fixes are unlikely.
Requires PyTorch to be installed separately; no runtime dependencies listed in package metadata, so you must manage PyTorch installation yourself.
License in practice
Licensed under Apache (permissive), so you may use and modify the code freely in commercial or private projects without restriction, though you must retain license notices.
Quickstart
pip install efficientnet-pytorch
from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_pretrained('efficientnet-b0')
# For adversarial training variant:
model = EfficientNet.from_pretrained('efficientnet-b0', advprop=True)Verify before relying
- Whether the package remains compatible with recent PyTorch versions (last tested in 2021).
- Whether pretrained weights are still accessible from the original sources.
- Whether EfficientNetV2 implementation mentioned in April 2021 update was ever completed and released.
Package facts
| License | Apache permissive |
| Python support | Supports the current Python release >=3.5.0 |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Abandoned 1,947 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 162,491 / month, #10,599 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.6 |
Evidence: efficientnet_pytorch-0.7.1.tar.gz
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