# FCOS
FCOS detects objects per pixel instead of relying on a set of predefined anchor boxes, predicting a box and a centerness score at every location on the feature map. LibreYOLO ports the torchvision implementation for detection.
Tasks: Detection. Install: pip install libreyolo.
Verified against LibreYOLO v1.5.0.

## Install

FCOS 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("LibreFCOSr50.pt")
result = model(SAMPLE_IMAGE, save=True)

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

**CLI**

```bash
libreyolo predict model=LibreFCOSr50.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. Calling the model with no
threshold arguments applies FCOS's own published defaults, `conf=0.2`,
`iou=0.6` and `max_det=100`; pass any of the three to override them. FCOS
keeps a final NMS step over its per-pixel predictions. See
[prediction](/docs/predict) for sources, streaming and result handling.

## Variants

One size: ResNet-50 with a feature pyramid, the only variant this family
recognizes.

## 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("LibreFCOSr50.pt")
metrics = model.val(data="my-dataset.yaml")

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

**CLI**

```bash
libreyolo val model=LibreFCOSr50.pt data=my-dataset.yaml
```

## Export

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

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

FCOS exports to ONNX, TorchScript and OpenVINO. FCOS preserves the source
aspect ratio before the graph runs, so LibreYOLO forces `dynamic=True` for the
ONNX and OpenVINO paths regardless of what is passed, to keep the graph valid
for padded input 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("LibreFCOSr50.pt")
model.export(format="onnx", imgsz=800)
model.export(format="torchscript", imgsz=800)
```

**CLI**

```bash
libreyolo export model=LibreFCOSr50.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("LibreFCOSr50.onnx")
result = model(SAMPLE_IMAGE)

print(result.boxes.xyxy)
```

## Checkpoints

Every published weight file for this family.

| File | Input (px) | Task | Weights license |
| --- | --- | --- | --- |
| `LibreFCOSr50.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: FCOS, 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 published checkpoint used for parity testing is 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 the Hugging Face mirror ships the BSD text on that implied basis and repeats the caveat rather than issuing an explicit checkpoint-specific grant.

## Citation

```bibtex
@inproceedings{tian2019fcos,
  title   =  {{FCOS}: Fully Convolutional One-Stage Object Detection},
  author  =  {Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong},
  booktitle =  {Proc. Int. Conf. Computer Vision (ICCV)},
  year    =  {2019}
}

@article{tian2021fcos,
  title   =  {{FCOS}: A Simple and Strong Anchor-free Object Detector},
  author  =  {Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong},
  booktitle =  {IEEE T. Pattern Analysis and Machine Intelligence (TPAMI)},
  year    =  {2021}
}
```

Copied from https://github.com/tianzhi0549/FCOS#citations


## FCOS architecture

- FCOS ResNet-50: [SVG](/diagrams/models/fcos/r50.svg), [interactive diagram](/diagrams/models/fcos/r50.html)