Keypoint Detection
ultralytics
carfusion
pose
vehicle-keypoints
yolo
pose-estimation
computer-vision
sim-to-real
Eval Results (legacy)
Instructions to use kiselyovd/vehicle-keypoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use kiselyovd/vehicle-keypoints with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("kiselyovd/vehicle-keypoints", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Publish reviewed artifact evidence from d8fb4c264155
Browse files- artifact-manifest.json +76 -0
- reports/metrics.json +26 -0
artifact-manifest.json
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{
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"schema_version": 1,
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"project": "vehicle-keypoints",
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"hub": {
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"repo_id": "kiselyovd/vehicle-keypoints",
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"repo_type": "model",
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"revision": "d277c2d8c9b5ee49d9fed85fe43bdd72b16a0f06",
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"files": [
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{
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"path": "weights.pt",
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"role": "weights",
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"sha256": "6fab133b352391b24209204c55733558a3c18ef36be591ecf32d0a7ca3c27235"
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}
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]
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},
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"input": {
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"type": "RGB road image",
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"image_size": 640,
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"box_confidence_default": 0.25,
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"implementation": "Ultralytics predict (letterbox, RGB tensor, /255)"
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},
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"output": {
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"keypoints": 14,
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"bbox": "xywh in CLI, xyxy in viewer JSON",
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"scores": "detector/keypoint confidence, distinct from annotation visibility codes"
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},
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"dataset": {
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"source": "https://www.cs.cmu.edu/~ILIM/projects/IM/CarFusion/cvpr2018/index.html",
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"test_images_reported": 12761,
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"training_membership_verified": false,
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"revision_recorded": false
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},
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"evaluation": {
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"status": "reported",
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"label": "Stored CarFusion evaluation; not re-executed as part of this audit",
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"scope": "12761 source test frames, OKS sigmas 0.05, PCK threshold 0.05 of bbox diagonal. Historical report is tracked; original training membership and immutable dataset revision were not recorded. Curated demo outputs are separate illustrations.",
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"training_membership_verified": false,
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"report": "reports/metrics.json",
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"report_sha256": "71efbee0f57b4e6061bf8804572f5b7bff1900bd46f58b29ddac50c3112eff59",
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"metrics": [
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{
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"name": "OKS-mAP",
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"pointer": "/oks_map"
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},
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{
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"name": "OKS-mAP50",
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"pointer": "/oks_map_50"
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},
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{
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"name": "PCK@0.05",
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"pointer": "/pck_0.05"
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}
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]
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},
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"licenses": {
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"code": {
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"id": "MIT",
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"path": "LICENSE"
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},
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"dataset": {
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"id": "source-specific",
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"source": "https://www.cs.cmu.edu/~ILIM/projects/IM/CarFusion/cvpr2018/index.html"
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},
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"weights": {
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"author_declaration": "MIT",
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"upstream": "Ultralytics YOLO26-pose",
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"upstream_terms": "AGPL-3.0 or applicable commercial license",
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"source": "https://docs.ultralytics.com/help/FAQ/",
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"scope": "Author declaration does not remove upstream obligations."
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}
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},
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"demo": {
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"url": "https://huggingface.co/spaces/kiselyovd/vehicle-keypoints-demo",
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"model_revision": "d277c2d8c9b5ee49d9fed85fe43bdd72b16a0f06"
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}
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}
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reports/metrics.json
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{
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"oks_map": 0.5038477111813657,
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"oks_map_50": 0.7036296602254928,
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"oks_map_75": 0.5956094705857681,
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"oks_map_medium": 0.004185689310737217,
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"oks_map_large": 0.5128098889107303,
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"pck_0.05": 0.7606000740832202,
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"per_keypoint_pck_0.05": [
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0.7843014280200695,
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0.7918530274611648,
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0.78356743694306,
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0.75692857852389,
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0.7484740465843058,
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0.7745107584461104,
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0.7600806451612904,
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0.7098129643863694,
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0.37542662116040953,
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0.7988692232055064,
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0.8096430500319144,
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0.7176726316853705,
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0.7085530611219929,
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0.0
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],
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"test_size": 12761,
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"n_predictions": 42706
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}
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