efficientnet
EfficientNet model re-implementation. Keras and TensorFlow Keras.
Decision gist · record as of 2026-08-14
Yes, if you need a pre-trained EfficientNet for Keras/TensorFlow and are working with compatible framework versions. The low install friction and permissive license make it straightforward to add. However, the package is dormant (last release September 2020, last commit January 2024), so verify compatibility with your current Keras/TensorFlow versions before committing to production use. For active maintenance and broader framework support, consider alternatives that are actively maintained.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Keras >= 2.2.0 or TensorFlow >= 1.12.0 to be installed separately; the package itself does not declare these as direct dependencies.
- Low install friction with only two runtime dependencies (keras-applications and scikit-image).
- However, the package is dormant—last release was 2020-09-15 and last commit 2024-01-24—so it may not receive updates for newer Keras or TensorFlow versions.
License · maintenance · safety
Apache License 2.0 (permissive) — Licensed under Apache License 2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions.
last release 2020-09-15 (2159 days) · last repo commit 2024-01-24 · 2,099 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 138,661 downloads/mo, #11,322 on PyPI
Alternatives
Verify before relying
pip install efficientnet
import efficientnet.keras as efn
model = efn.EfficientNetB0(weights='imagenet')- Whether the package works with current TensorFlow/Keras versions (last release 2020-09-15, last commit 2024-01-24).
- Whether pre-trained weights are still accessible and up-to-date.
- Compatibility with modern Python versions beyond what 'supports_current' indicates.
What it is and what it does
EfficientNet is a family of convolutional neural network architectures (B0 through B7) designed to achieve high ImageNet accuracy with significantly fewer parameters and computational cost than competing models. This package provides Keras and TensorFlow Keras implementations with pre-trained weights, allowing you to load a model and use it immediately for image classification, transfer learning, or as a backbone for custom tasks.
The package depends on keras-applications for model utilities and scikit-image for image processing. It supports both standalone Keras and TensorFlow's integrated Keras API. Models can be initialized with ImageNet weights or noisy-student weights; the description notes a major update on 24 July 2019 that unified support across both frameworks, though models trained before that date require version 0.0.4.
Use it for
- Load a pre-trained EfficientNet model for immediate image classification on new images without retraining.
- Use EfficientNet as a feature extractor backbone for transfer learning on custom image datasets.
- Compare model variants (B0–B7) to balance accuracy and inference speed for deployment constraints.
- Fine-tune a pre-trained EfficientNet on domain-specific images with limited labeled data.
- Benchmark model efficiency: parameter count and FLOPS against other architectures like ResNet.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need a pre-trained EfficientNet for Keras/TensorFlow and are working with compatible framework versions.
The low install friction and permissive license make it straightforward to add. However, the package is dormant (last release September 2020, last commit January 2024), so verify compatibility with your current Keras/TensorFlow versions before committing to production use. For active maintenance and broader framework support, consider alternatives that are actively maintained.
Install
efficientnet on PyPI
Before you install
Low install friction with only two runtime dependencies (keras-applications and scikit-image). However, the package is dormant—last release was 2020-09-15 and last commit 2024-01-24—so it may not receive updates for newer Keras or TensorFlow versions.
Requires Keras >= 2.2.0 or TensorFlow >= 1.12.0 to be installed separately; the package itself does not declare these as direct dependencies.
License in practice
Licensed under Apache License 2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions.
Quickstart
pip install efficientnet
import efficientnet.keras as efn
model = efn.EfficientNetB0(weights='imagenet')Verify before relying
- Whether the package works with current TensorFlow/Keras versions (last release 2020-09-15, last commit 2024-01-24).
- Whether pre-trained weights are still accessible and up-to-date.
- Compatibility with modern Python versions beyond what 'supports_current' indicates.
Package facts
| License | Apache License 2.0 permissive |
| Python support | Supports the current Python release >=3.0.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packageskeras-applicationsscikit-image |
| Maintenance | Dormant 2,159 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 138,661 / month, #11,322 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 :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPy |
Evidence: efficientnet-1.1.1-py3-none-any.whl
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See also efficientnet-pytorch · Keras-Applications · tf-keras-nightly · tf-keras · keras-hub · keras · pretrainedmodels · keras-nightly · keras-nlp · tensorflow-model-optimization