Pedsense Yolo26m Pose Jaad 10e
A pedestrian keypoint detection model fine-tuned on the JAAD (Joint Attention in Autonomous Driving) dataset using the PedSense-AI framework. Detects pedestrians and predicts 17 COCO body keypoints per person.
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 46,7 MB
- Versión
- vBest
- Actualizado
- 21 oct 2026
- Fuente
- Hugging Face
Descripción de la fuente
PedSense -- YOLO26m-Pose Pedestrian Keypoint Model (JAAD, 10 epochs)
Overview
A pedestrian keypoint detection model fine-tuned on the JAAD (Joint Attention in Autonomous Driving) dataset using the PedSense-AI framework. Detects pedestrians and predicts 17 COCO body keypoints per person.
Keypoint labels were generated by running the pretrained yolo11n-pose model on JAAD dashcam frames (pseudo-labelling). The model was then fine-tuned from yolo26m-pose on those labels.
Model Details
| Property | Value |
|---|---|
| Architecture | YOLO26m-Pose |
| Task | Keypoint detection (1 class: pedestrian, 17 COCO keypoints) |
| Dataset | JAAD |
| Epochs | 10 |
| Image Size | 640x640 |
| Batch Size | 16 |
| Train/Val Split | 80/20 (video-level) |
| kpt_shape | [17, 3] |
Performance (Best checkpoint -- epoch 10)
Box metrics
| Metric | Value |
|---|---|
| Precision | 0.949 |
| Recall | 0.906 |
| mAP@50 | 0.971 |
| mAP@50-95 | 0.853 |
Pose metrics
| Metric | Value |
|---|---|
| Precision | 0.907 |
| Recall | 0.860 |
| mAP@50 | 0.912 |
| mAP@50-95 | 0.649 |
COCO Keypoint Order (17 points)
| Index | Keypoint |
|---|---|
| 0 | Nose |
| 1 | Left eye |
| 2 | Right eye |
| 3 | Left ear |
| 4 | Right ear |
| 5 | Left shoulder |
| 6 | Right shoulder |
| 7 | Left elbow |
| 8 | Right elbow |
| 9 | Left wrist |
| 10 | Right wrist |
| 11 | Left hip |
| 12 | Right hip |
| 13 | Left knee |
| 14 | Right knee |
| 15 | Left ankle |
| 16 | Right ankle |
Usage
from ultralytics import YOLO
model = YOLO("weights/best.pt")
results = model("image.jpg")
# Access keypoints
for r in results:
print(r.keypoints.xy) # pixel coordinates (N, 17, 2)
print(r.keypoints.conf) # confidence per keypoint (N, 17)
Training
Keypoint labels were generated with preprocess pose, then the model was trained using the PedSense-AI framework:
# Step 1: Extract frames
uv run pedsense preprocess frames
# Step 2: Generate pose labels (pseudo-labelling with yolo11n-pose)
uv run pedsense preprocess pose
# Step 3: Train yolo26m-pose on generated labels
uv run pedsense train -m yolo-pose -n my_pose_model -e 10 -b 16 --yolo-variant yolo26m-pose
License
AGPL-3.0
Related Models
- pedsense-yolo11m-pose-jaad-10e -- YOLO11m-Pose keypoint model (10 epochs)
- pedsense-yolo26m-detector-jaad-20e -- 1-class pedestrian detector (20 epochs)
- pedsense-yolo26m-detector-jaad-aug-10e -- augmented detector (10 epochs)
- pedsense-yolo26-jaad-10e -- 2-class crossing intent classifier
Fuentes
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Fuentes
1 fuenteVerificado 21 oct
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Artefactos del modelo
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Extractos de fuentes
2 extractos--- license: agpl-3.0 tags: - pose-estimation - keypoint-detection - pedestrian - yolo - autonomous-driving datasets: - JAAD --- # PedSense -- YOLO26m-Pose Pedestrian Keypoint Model (JAAD, 10 epochs) ## Overview A pedestrian keypoint detection model fine-tuned on the JAAD (Joint Attention in Autonomous Driving) dataset using the PedSense-AI framework. Detects pedestrians and predicts 17 COCO body keypoints per person. Keypoint labels were generated by running the pretrained `yolo11n-pose` model on JAAD dashcam frames (pseudo-labelling). The model was then fine-tuned from `yolo26m-pose` on those labels. ## Model Details | Property | Value | |----------|-------| | **Architecture** | YOLO26m-Pose | | **Task** | Keypoint detection (1 class: `pedestrian`, 17 COCO keypoints) | | **Dataset** | JAAD | | **Epochs** | 10 | | **Image Size** | 640x640 | | **Batch Size** | 16 | | **Train/Val Split** | 80/20 (video-level) | | **kpt_shape** | [17, 3] | ## Performance (Best checkpoint -- epoch 10) ### Box metrics | Metric | Value | |--------|-------| | **Precision** | 0.949 | | **Recall** | 0.906 | | **mAP@50** | 0.971 | | **mAP@50-95** | 0.853 | ### Pose metrics | Metric | Value | |--------|-------| | **Precision** | 0.907 | | **Recall** | 0.860 | | **mAP@50** | 0.912 | | **mAP@50-95** | 0.649 | ## COCO Keypoint Order (17 points) | Index | Keypoint | |-------|----------| | 0 | Nose | | 1 | Left eye | | 2 | Right eye | | 3 | Left ear | | 4 | Right ear | | 5 | Left shoulder | | 6 | Right shoulder | | 7 | Left elbow | | 8 | Right elbow | | 9 | Left wrist | | 10 | Right wrist | | 11 | Left hip | | 12 | Right hip | | 13 | Left knee | | 14 | Right knee | | 15 | Left ankle | | 16 | Right ankle | ## Usage ```python from ultralytics import YOLO model = YOLO("weights/best.pt") results = model("image.jpg") # Access keypoints for r in results: print(r.keypoints.xy) # pixel coordinates (N, 17, 2) print(r.keypoints.conf) # confidence per keypoint (N, 17) ``` ## Training Keypoint labels were generated with `preprocess pose`, then the model was trained using the PedSense-AI framework: ```bash # Step 1: Extract frames uv run pedsense preprocess frames # Step 2: Generate pose labels (pseudo-labelling with yolo11n-pose) uv run pedsense preprocess pose # Step 3: Train yolo26m-pose on generated labels uv...
Source context: 0 downloads · 0 likes · Pipeline keypoint-detection · Repo JcProg/pedsense-yolo26m-pose-jaad-10e
