EfficientDet

EfficientDet pairs an EfficientNet backbone with a repeated bi-directional feature pyramid network (BiFPN) and scales depth, width and resolution together across five sizes. LibreYOLO ships it as an inference-only detector.

Tasks: detection
Sizes
Install: pip install libreyolo
Support tier: Inference only, since v. Predict, validate and export only. Training features do not apply.
Upstream: EfficientDet by Google Research, Apache-2.0. Paper, source
Licenses: Code Apache-2.0, weights Apache-2.0. Commercial use

Install

EfficientDet needs no optional extra. Everything it imports is in the base install.

bash

pip install libreyolo

Predict

Weights download from Hugging Face on first use and are cached locally.

Python

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreEfficientDetd0.pt")result = model(SAMPLE_IMAGE, save=True) for box in result.boxes:    print(box.cls, box.conf, box.xyxy)

CLI

libreyolo predict model=LibreEfficientDetd0.pt source=https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg save=True

The returned Results object is the one every family returns, so swapping in a different detector is a one line change. EfficientDet decodes anchor-based candidates and then runs class-wise non-maximum suppression, so conf, iou and max_det all have a real effect here. See prediction for sources, streaming and result handling.

Variants

Five sizes, D0 through D4. Each step up pairs a larger EfficientNet backbone with a deeper, wider BiFPN and a deeper prediction head, so parameter count and compute grow together, following the paper's compound-scaling rule.

Validate

val() returns a dictionary of metrics/ keys covering precision, recall, mAP 50 and mAP 50-95, measured against any dataset in the format you trained on.

Python

from libreyolo import LibreYOLO model = LibreYOLO("LibreEfficientDetd0.pt")metrics = model.val(data="my-dataset.yaml") print(metrics["metrics/mAP50-95"])print(metrics["metrics/mAP50"])

CLI

libreyolo val model=LibreEfficientDetd0.pt data=my-dataset.yaml

Export

Task	ONNX	TorchScript	ExecuTorch	TensorRT	OpenVINO	Paddle	MNN	RKNN	ncnn	TFLite	CoreML	Core AI
Detection	Detection to ONNX: supported.	Detection to TorchScript: supported.	Detection to ExecuTorch: supported.	Detection to TensorRT: supported.	Detection to OpenVINO: supported.	Detection to Paddle: not supported	Detection to MNN: not supported	Detection to RKNN: not supported	Detection to ncnn: supported.	Detection to TFLite: not supported	Detection to CoreML: not supported	Detection to Core AI: not supported

An exported artifact loads back through LibreYOLO() on its file suffix, so a .onnx or .engine file behaves like a checkpoint and returns the same Results.

Python

from libreyolo import LibreYOLO model = LibreYOLO("LibreEfficientDetd0.pt")model.export(format="onnx")model.export(format="tensorrt", half=True)

CLI

libreyolo export model=LibreEfficientDetd0.pt format=onnxlibreyolo export model=LibreEfficientDetd0.pt format=tensorrt half=True

Use the exported file

from libreyolo import LibreYOLO # The factory routes on the file suffix, so an exported artifact loads# like any checkpoint and returns the same Results object.model = LibreYOLO("LibreEfficientDetd0.onnx")result = model(SAMPLE_IMAGE) print(result.boxes.xyxy)

Checkpoints

Every published weight file for this family.

File	Input (px)	Weights license
Detection
LibreEfficientDetd0.pt		apache-2.0
LibreEfficientDetd1.pt		apache-2.0
LibreEfficientDetd2.pt		apache-2.0
LibreEfficientDetd3.pt		apache-2.0
LibreEfficientDetd4.pt		apache-2.0

Every file above exists in the LibreYOLO org today and downloads on first use.

Licensing

Check the license on the Hugging Face repository of the specific weights you download. Every checkpoint in the LibreYOLO org carries one, and they are not always the same across a family. That repository is the authoritative source; the summary below describes what applied when this page was last verified.

This is a description of the licenses involved, not legal advice. If the answer matters commercially, read the licenses yourself and take your own counsel.

Original work: EfficientDet, Google Research
Upstream license: Apache-2.0
Upstream source: github.com/rwightman/efficientdet-pytorch
LibreYOLO code: MIT
Weights: Apache-2.0, republished at huggingface.co/LibreYOLO
Interpretation: Apache-2.0 is a permissive license, so these weights can be used in commercial and closed-source products. It asks you to keep its license text and attribution notices with any copy of the weights you redistribute, and it grants a patent license. It places no obligation on your own application code. LibreYOLO's D0-D4 checkpoints are converted from Google's official TensorFlow-trained release assets through the Apache-2.0 rwightman/efficientdet-pytorch project; those release assets carry no separate per-checkpoint license, so redistribution here rests on Apache-2.0 implied by the releasing project, the same basis rwightman/efficientdet-pytorch itself uses for its own converted weights.

LibreYOLO's D0-D4 checkpoints are converted through the Apache-2.0 rwightman/efficientdet-pytorch project, which itself mirrors the official TensorFlow-trained weights from google/automl without changing learned tensors. No source from the LGPL-licensed zylo117/Yet-Another-EfficientDet-Pytorch project was consulted or used.