> This is the documentation for LibreYOLO 1.5.0. Current version: https://www.libreyolo.com/docs/models/retinanet

# RetinaNet
RetinaNet is a one-stage detector trained with focal loss, which down-weights easy negatives so a dense grid of anchors no longer needs a separate proposal stage to stay accurate. LibreYOLO ports the torchvision implementation for detection.
Tasks: Detection. Install: pip install libreyolo.
Verified against LibreYOLO v1.5.0.

## Install

RetinaNet 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**

```python
from libreyolo import LibreYOLO, SAMPLE_IMAGE

model = LibreYOLO("LibreRetinaNetr50v2.pt")
result = model(SAMPLE_IMAGE, save=True)

for box in result.boxes:
    print(box.cls, box.conf, box.xyxy)
```

**CLI**

```bash
libreyolo predict model=LibreRetinaNetr50v2.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. `conf` and `iou` set the
confidence and NMS thresholds; RetinaNet keeps its upstream NMS step over the
dense anchor grid. See [prediction](/docs/v1.5.0/predict) for sources, streaming and
result handling.

## Variants

Two sizes, both ResNet-50 with a feature pyramid: `r50` is the original
head, and `r50v2` replaces it with a GroupNorm head and a wider P6 block fed
from the backbone's last stage instead of the FPN output.

## 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**

```python
from libreyolo import LibreYOLO

model = LibreYOLO("LibreRetinaNetr50v2.pt")
metrics = model.val(data="my-dataset.yaml")

print(metrics["metrics/mAP50-95"])
print(metrics["metrics/mAP50"])
```

**CLI**

```bash
libreyolo val model=LibreRetinaNetr50v2.pt data=my-dataset.yaml
```

## Export

| Task | ONNX | TorchScript | ExecuTorch | TensorRT | OpenVINO | Paddle | MNN | RKNN | ncnn | TFLite | CoreML | Core AI |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Detection | yes |  |  |  |  |  |  |  |  |  |  |  |

A "yes" means the export is supported. An empty cell means the exporter refuses that combination.

RetinaNet exports to ONNX only, at batch size 1. RetinaNet resizes to a
variable, aspect-preserved input, so LibreYOLO forces `dynamic=True`
regardless of what is passed, to keep the graph valid for sources of
different shapes. An exported `.onnx` file loads back through `LibreYOLO()`
on its file suffix and returns the same `Results`.

**Python**

```python
from libreyolo import LibreYOLO

model = LibreYOLO("LibreRetinaNetr50v2.pt")
model.export(format="onnx", imgsz=800)
```

**CLI**

```bash
libreyolo export model=LibreRetinaNetr50v2.pt format=onnx imgsz=800
```

**Use the exported file**

```python
from libreyolo import LibreYOLO, SAMPLE_IMAGE

# The factory routes on the file suffix, so an exported artifact loads
# like any checkpoint and returns the same Results object.
model = LibreYOLO("LibreRetinaNetr50v2.onnx")
result = model(SAMPLE_IMAGE)

print(result.boxes.xyxy)
```

## Checkpoints

Every published weight file for this family.

| File | Input (px) | Task | Weights license |
| --- | --- | --- | --- |
| `LibreRetinaNetr50.pt` | 800 | Detection | bsd-3-clause |
| `LibreRetinaNetr50v2.pt` | 800 | Detection | bsd-3-clause |

## Licensing

Check the license on the Hugging Face repository of the specific weights you download. That repository is authoritative and licenses are not always uniform across a family. This is a description of the licenses involved, not legal advice.

- Original work: RetinaNet, PyTorch
- Upstream license: BSD-3-Clause
- Upstream source: https://github.com/pytorch/vision
- LibreYOLO code: BSD-3-Clause
- Weights: BSD-3-Clause, republished at https://huggingface.co/LibreYOLO
- Interpretation: BSD-3-Clause is a permissive license, so this code can be used in commercial and closed-source products with no obligation on your own application code. It asks only that you keep the copyright notice and disclaimer with any copy you redistribute, and it carries no patent grant. The two published checkpoints used for parity testing are not distributed in the LibreYOLO source tree: torchvision's own documentation notes that a pretrained model's terms may depend on its training data, so each Hugging Face mirror ships the BSD text on that implied basis and repeats the caveat rather than issuing an explicit checkpoint-specific grant.


## RetinaNet architecture

- RetinaNet R50: [SVG](/diagrams/models/retinanet/r50.svg), [interactive diagram](/diagrams/models/retinanet/r50.html)
- RetinaNet R50V2: [SVG](/diagrams/models/retinanet/r50v2.svg), [interactive diagram](/diagrams/models/retinanet/r50v2.html)