Model Card: Faster R-CNN R101-FPN on ZOD (Vehicle, Pedestrian, VulnerableVehicle)

Model Details

• Architecture: Faster R-CNN with ResNet-101 + FPN (Detectron2 model zoo config COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml)
• Framework: Detectron2

Training Data

• Source: Zenseact Open Dataset (ZOD), converted to COCO
• Classes kept: Vehicle, Pedestrian, VulnerableVehicle
• Filtering: exclude occlusion levels Heavy and VeryHeavy (keep None/Medium/Light); drop boxes with height < 25 px
• Split: original ZOD train is split into train/val by 1 km lat/lon tiles (80/20; 69,090 train / 20,882 val) to reduce spatial leakage and keep weather/road_type/time_of_day distributions similar; original ZOD val is used as test
• Anonymization: used the conversion script default (BLUR)

Training Procedure

• Augmentations: random horizontal flip, brightness/contrast/saturation jitter, ResizeShortestEdge
• Solver: IMS_PER_BATCH=16, BASE_LR=0.001, MAX_ITER=150000, STEPS=(80000, 110000), WARMUP_ITERS=1000
• Evaluation: COCO evaluator every 1000 iterations

Evaluation Results

• AP: 34.2
• AP50: 59.4
• AP75: 33.3
• AP-Pedestrian: 23.2
• AP-Vehicle: 55.2
• AP-VulnerableVehicle: 24.2
• APl: 53.3
• APm: 31.4
• APs: 10.8

Usage (Detectron2)

Limitations

• Trained on only three classes; other ZOD object types are not represented.
• Heavy/VeryHeavy occlusions and very small objects (<25 px height) are filtered out and may perform poorly at inference time.