# PG-COCO-Pose

PG-COCO-Pose is a public keypoint detection model by Pictograph Research on Pictograph, trained on the COCO 2017 Pose dataset and exportable to ONNX for inference. Reported OKS of 0.732.

## Overview

- Type: keypoint detection model
- Task: Keypoint detection (https://pictograph.io/explore/task/keypoint-detection)
- Architecture: RF-DETR Keypoint Preview
- Owner: Pictograph Research
- Trained on: COCO 2017 Pose (https://pictograph.io/datasets/pictograph-research/coco-2017-pose)
- License: Custom License
- Export: ONNX
- Stars: 1
- Page: https://pictograph.io/models/pictograph-research/pg-coco-pose
- Hugging Face: https://huggingface.co/pictograph/pg-coco-pose

## Evaluation metrics

- OKS: 73.2%
- OKS50: 89.1%
- OKS75: 79.2%
- recall: 78.8%

## About

# PG-COCO-Pose

> A keypoint detection model (RF-DETR Keypoint Preview), locating each person and its 17 keypoints, scoring 73.2% OKS.

**[Pictograph Research](/org/pictograph-research)** · Keypoint Detection · RF-DETR Keypoint Preview · FP32 · a custom license · Public

## Overview

This is a keypoint detection model built on **RF-DETR Keypoint Preview**, trained on the [COCO 2017 Pose](/datasets/pictograph-research/coco-2017-pose) dataset. It expects **576x576** input and runs in FP32 precision. It finds each person and its 17 keypoints: the Skeleton section lists them in output order, with the joints each connects to.

## Performance

Headline: **73.2% OKS**.

| Metric | Value |
| :--- | ---: |
| OKS | 73.2% |
| OKS@50 | 89.1% |
| OKS@75 | 79.2% |
| Recall | 78.8% |

OKS is COCO's keypoint AP: the average precision over object keypoint similarity (OKS) thresholds 0.50 to 0.95. OKS@50 and OKS@75 are the AP at one threshold; Recall is the average recall over 0.50 to 0.95.

## Skeleton

Each object the model finds is one instance: its keypoints share an `instance_id`, are named for their joint and carry their own confidence. The keypoint index is the model's output order.

| # | Keypoint | Connects to |
| ---: | :--- | :--- |
| 0 | nose | left_eye, right_eye |
| 1 | left_eye | right_eye, nose, left_ear |
| 2 | right_eye | left_eye, nose, right_ear |
| 3 | left_ear | left_eye, left_shoulder |
| 4 | right_ear | right_eye, right_shoulder |
| 5 | left_shoulder | left_hip, right_shoulder, left_elbow, left_ear |
| 6 | right_shoulder | right_hip, left_shoulder, right_elbow, right_ear |
| 7 | left_elbow | left_shoulder, left_wrist |
| 8 | right_elbow | right_shoulder, right_wrist |
| 9 | left_wrist | left_elbow |
| 10 | right_wrist | right_elbow |
| 11 | left_hip | left_knee, right_hip, left_shoulder |
| 12 | right_hip | right_knee, left_hip, right_shoulder |
| 13 | left_knee | left_ankle, left_hip |
| 14 | right_knee | right_ankle, right_hip |
| 15 | left_ankle | left_knee |
| 16 | right_ankle | right_knee |

## Training

| Setting | Value |
| :--- | :--- |
| Base dataset | [COCO 2017 Pose](/datasets/pictograph-research/coco-2017-pose) |
| Export | COCO 2017 Pose-select |
| Dataset size | 10,799 images |
| Input size | 576x576 |
| Epochs | 12 |
| Batch size | 4 |
| Learning rate | 0.0001 |
| Model size | preview |
| Precision | FP32 |
| Version | 1.0.0 |

## Use this model

Run inference with the Pictograph SDK:

```python
from pictograph import Client, get_model, KeypointModel, KeypointResult

client = Client(api_key="pk_live_...")  # Settings -> API Keys

# Downloads + caches the weights on first use
model: KeypointModel = get_model(
    name="PG-COCO-Pose",
    task="keypoint_detection",
    format="onnx",
    precision="fp32",
    device="cpu",
    client=client,
)
result: KeypointResult = model.predict(image="photo.jpg")
print(result)
```

```python
# Fully offline (no API key) once you have the weights + config.json
from pictograph import load_model, KeypointModel, KeypointResult

model: KeypointModel = load_model(
    "/path/to/weights.onnx",
    "/path/to/config.json",
    task="keypoint_detection",
    format="onnx",
)
result: KeypointResult = model.predict(image="photo.jpg")
```

- Full inference guide: [local inference docs](https://pictograph.io/docs/local-inference).
- REST + CLI: [models API reference](https://pictograph.io/docs/api-reference/models).
- Deploy it as an always-on endpoint: [deployments](https://pictograph.io/docs/deployments).

## License

Released under a custom license.


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Free to view. Sign in at https://app.pictograph.io to fork this model, run batch inference, or deploy it as a live endpoint.