PG-HandPose

Pictograph Research · Keypoint detection · Trained 32m ago · CC-BY-NC-SA-4.0

Use this model

PG-HandPose

Environment

Weights

ONNX SafeTensors PyTorch ExecuTorch TensorRT Engine

Precision

FP32 FP16

Model weights Model config

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-HandPose",
    task="keypoint_detection",
    format="onnx",
    client=client,
)
result: KeypointResult = model.predict(image="photo.jpg")

for instance in result.instances:   # joints grouped by instance_id
    for joint in instance:
        p = joint.keypoint
        print(joint.name, round(p.x), round(p.y))

from pictograph import load_model, KeypointModel, KeypointResult

# Download the weights + config with the buttons above
model: KeypointModel = load_model(
    weights="/path/to/weights.onnx",
    config="/path/to/config.json",
    task="keypoint_detection",
    format="onnx",
    device="auto",
)
result: KeypointResult = model.predict(image="photo.jpg")

for instance in result.instances:   # joints grouped by instance_id
    for joint in instance:
        p = joint.keypoint
        print(joint.name, round(p.x), round(p.y))

Deploy this model to Pictograph for an always-on, autoscaling /predict endpoint. Then call it from anywhere:

curl -X POST "https://workflows.pictograph.io/your-endpoint/predict" \
  -H "Authorization: Bearer pk_deploy_..." \
  -F "file=@frame.jpg"

from pictograph import DeploymentClient, KeypointResult

client = DeploymentClient(
    endpoint="https://workflows.pictograph.io/your-endpoint/predict",
    api_key="pk_deploy_...",  # from your deployment's settings
    task="keypoint_detection",
)
result: KeypointResult = client.infer(image="frame.jpg")

for instance in result.instances:   # joints grouped by instance_id
    for joint in instance:
        p = joint.keypoint
        print(joint.name, round(p.x), round(p.y))

pictograph deployments predict \
  <deployment-id> \
  frame.jpg \
  --token pk_deploy_... \
  --endpoint "https://workflows.pictograph.io/your-endpoint/predict"

Model Card

PG-HandPose

A keypoint detection model (RF-DETR Keypoint Preview), locating each hand and its 21 keypoints, scoring 81.8% OKS.

Pictograph Research · Keypoint Detection · RF-DETR Keypoint Preview · FP32 · CC-BY-NC-SA-4.0 · Public

Overview

This is a keypoint detection model built on RF-DETR Keypoint Preview, trained on the Hand Keypoints dataset. It expects 576x576 input and runs in FP32 precision. It finds each hand and its 21 keypoints: the Skeleton section lists them in output order, with the joints each connects to.

Performance

Headline: 81.8% OKS.

Metric	Value
OKS	81.8%
OKS@50	92.7%
OKS@75	84.0%
Recall	85.1%

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	wrist	thumb_cmc, index_finger_mcp, pinky_mcp
1	thumb_cmc	wrist, thumb_mcp
2	thumb_mcp	thumb_cmc, thumb_ip
3	thumb_ip	thumb_mcp, thumb_tip
4	thumb_tip	thumb_ip
5	index_finger_mcp	wrist, index_finger_pip, middle_finger_mcp
6	index_finger_pip	index_finger_mcp, index_finger_dip
7	index_finger_dip	index_finger_pip, index_finger_tip
8	index_finger_tip	index_finger_dip
9	middle_finger_mcp	index_finger_mcp, middle_finger_pip, ring_finger_mcp
10	middle_finger_pip	middle_finger_mcp, middle_finger_dip
11	middle_finger_dip	middle_finger_pip, middle_finger_tip
12	middle_finger_tip	middle_finger_dip
13	ring_finger_mcp	middle_finger_mcp, ring_finger_pip, pinky_mcp
14	ring_finger_pip	ring_finger_mcp, ring_finger_dip
15	ring_finger_dip	ring_finger_pip, ring_finger_tip
16	ring_finger_tip	ring_finger_dip
17	pinky_mcp	ring_finger_mcp, wrist, pinky_pip
18	pinky_pip	pinky_mcp, pinky_dip
19	pinky_dip	pinky_pip, pinky_tip
20	pinky_tip	pinky_dip

Training

Setting	Value
Base dataset	Hand Keypoints
Export	Hand Keypoints-base
Dataset size	26,677 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:

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-HandPose",
    task="keypoint_detection",
    format="onnx",
    precision="fp32",
    device="cpu",
    client=client,
)
result: KeypointResult = model.predict(image="photo.jpg")
print(result)

# 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")