---
license: apache-2.0
library_name: libreyolo
pipeline_tag: object-detection
tags:
  - object-detection
  - efficientdet
  - pytorch
datasets:
  - detection-datasets/coco
---

# LibreEfficientDetd1

EfficientDet-D1 with an EfficientNet-B1 backbone, a weighted BiFPN, 6.63M
parameters, and a fixed 640x640 input, repackaged for
[LibreYOLO](https://github.com/LibreYOLO/libreyolo).

```python
from libreyolo import LibreYOLO

model = LibreYOLO("LibreEfficientDetd1.pt")
results = model.predict("image.jpg")
```

LibreYOLO ships this family for detection inference and validation. Training is
not implemented. ONNX, TorchScript, OpenVINO, and TensorRT exports have
trained-checkpoint prediction-parity coverage on the D0 family representative.

## Source

Converted from the official
[`tf_efficientdet_d1_40-a30f94af.pth`](https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1/tf_efficientdet_d1_40-a30f94af.pth)
release asset from
[`rwightman/efficientdet-pytorch`](https://github.com/rwightman/efficientdet-pytorch)
0.4.1 at commit `c6dff775a36cea0bf9b76c58e59f936411c5ce01`.
Copyright 2020 Ross Wightman. Licensed under the Apache License 2.0.

The native EfficientNet block implementation also follows
[`huggingface/pytorch-image-models`](https://github.com/huggingface/pytorch-image-models)
v1.0.28 at commit `8ef73809f622e0031bd7f4940265734aef8b9978`
(Apache-2.0). The original EfficientDet design is from Google Research's
Apache-2.0 [`google/automl`](https://github.com/google/automl/tree/master/efficientdet)
implementation.

Source checkpoint SHA-256:
`a30f94afc3326a6ef7a61c1657baefe3ea7168139006fbf0fe41807260b885b8`.

## Modifications

Checkpoint metadata wrap only. Learned parameter names, dtypes, and tensor
values are unchanged. See `weights/convert_efficientdet_weights.py` in the
[LibreYOLO source repository](https://github.com/LibreYOLO/libreyolo).

Strict loading succeeds with no missing or unexpected keys. Against `effdet`
0.4.1, identical fixed inputs produce bit-exact FP32 class and box feature
tensors, anchors, and all 5,000 decoded candidate rows (`max_abs_diff == 0.0`).
LibreYOLO maps the sparse 90-slot COCO head to a contiguous COCO-80 interface.

## Benchmarks

The pinned upstream model zoo reports **40.1 COCO val2017 box AP** for this
checkpoint. Independent accuracy and speed results will appear at
[visionanalysis.org/model/efficientdet-d1](https://www.visionanalysis.org/model/efficientdet-d1)
after EfficientDet is added to the Vision Analysis model catalog.

## Limitations

- Detection uses the fixed 640x640 evaluation canvas and COCO-80 classes.
- The focal-loss and anchor-matching training recipe is not included.
- TensorRT uses 3,840 pre-NMS candidates because TensorRT 10.x limits `TopK`;
  native, ONNX, TorchScript, and OpenVINO retain the upstream 5,000 candidates.

## License

Apache License 2.0. See the [`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE)
files in this repository. The upstream release does not attach a separate
checkpoint-specific license object; this mirror relies on Apache-2.0 as implied
by the releasing project, not a publisher-confirmed asset-specific grant.