effdet
EfficientDet for PyTorch
Decision gist · record as of 2026-08-14
Yes, if you need a stable, well-documented PyTorch object detector for inference or training on current PyTorch versions. The pretrained weights are solid (mAP scores published), the codebase is mature, and there are no known vulnerabilities. No, if you require active maintenance, support for the latest PyTorch features, or expect bug fixes—the project is dormant and last released in May 2023. For new projects, consider whether a more actively maintained detection library fits your timeline.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires torch and torchvision; GPU recommended for inference speed but not required.
- Image dimensions must be divisible by 128 on each axis.
- Low install friction with a pure Python wheel.
License · maintenance · safety
permissive license (permissive) — Apache 2.0 permissive license allows commercial and private use with minimal restrictions; you may use, modify, and distribute the code freely as long as you include the license notice.
last release 2023-05-21 (1181 days) · last repo commit 2024-08-29 · 1,654 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 530,296 downloads/mo, #6,156 on PyPI
Alternatives
Verify before relying
pip install effdet torch torchvision timm pycocotools omegaconf
import effdet
from effdet import get_detection_model
model = get_detection_model('tf_efficientdet_d0', pretrained=True)
model.eval()- Whether pretrained weights cover all model variants listed in the description or only a subset
- Current compatibility with PyTorch 2.0+ given dormant maintenance status since 2023
- Whether all timm backbones with feature_only support work as drop-in replacements or require configuration tuning
What it is and what it does
EfficientDet is a PyTorch port of Google's official TensorFlow object detection architecture, designed to balance speed and accuracy across multiple model scales. It provides pretrained weights for COCO detection and supports training on custom datasets (COCO, VOC, OpenImages) with a flexible configuration system. The core innovation is the BiFPN (Bidirectional Feature Pyramid Network), which you can customize via config—switching between depthwise separable and standard convolutions, adjusting activation functions, and choosing from any timm backbone that supports feature extraction.
The package includes training and validation scripts, focal loss with label smoothing, data loaders for multiple dataset formats, and evaluation tools based on TensorFlow Models. It supports PyTorch 1.6+ features like native DDP, SyncBN, and AMP, plus optional TorchScript compilation for speed. However, maintenance is dormant as of August 2024, so it is stable for inference and training on existing PyTorch versions but will not receive updates for new PyTorch releases or dependency changes.
Use it for
- Train an object detector on a custom dataset using a pretrained EfficientDet backbone and fine-tuning scripts
- Deploy a lightweight COCO-pretrained detector for real-time inference on edge devices or servers
- Experiment with different BiFPN configurations and backbone architectures without modifying core model code
- Evaluate detection performance on VOC 2007/2012 or OpenImages datasets using built-in evaluators
- Convert a trained model to TorchScript for production inference with reduced memory and latency
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need a stable, well-documented PyTorch object detector for inference or training on current PyTorch versions.
The pretrained weights are solid (mAP scores published), the codebase is mature, and there are no known vulnerabilities. No, if you require active maintenance, support for the latest PyTorch features, or expect bug fixes—the project is dormant and last released in May 2023. For new projects, consider whether a more actively maintained detection library fits your timeline.
Install
effdet on PyPI
Before you install
Low install friction with a pure Python wheel. Maintenance is dormant—last release was May 2023 and no commits since August 2024—so expect no active bug fixes or dependency updates, though the codebase is stable enough for inference and training on existing PyTorch versions.
Requires torch and torchvision; GPU recommended for inference speed but not required. Image dimensions must be divisible by 128 on each axis.
License in practice
Apache 2.0 permissive license allows commercial and private use with minimal restrictions; you may use, modify, and distribute the code freely as long as you include the license notice.
Quickstart
pip install effdet torch torchvision timm pycocotools omegaconf
import effdet
from effdet import get_detection_model
model = get_detection_model('tf_efficientdet_d0', pretrained=True)
model.eval()Verify before relying
- Whether pretrained weights cover all model variants listed in the description or only a subset
- Current compatibility with PyTorch 2.0+ given dormant maintenance status since 2023
- Whether all timm backbones with feature_only support work as drop-in replacements or require configuration tuning
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagestorchtorchvisiontimmpycocotoolsomegaconf |
| Maintenance | Dormant 1,181 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 530,296 / month, #6,156 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: effdet-0.4.1-py3-none-any.whl
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See also timm · mmdet · pytorchcv · efficientnet-pytorch · segmentation-models-pytorch · ultralytics-thop · facenet-pytorch · groundingdino-py · pytorch-fid · optimum-quanto