pallet
person
forklift
warehouse-a / run-01
day · 12k frames · cam ×2 · lidar
Ready
opensense
warehouse-quad · Vektor Robotics
Good afternoon, Ada.
Here’s what your robot has seen and how well your models see it.
What the robot saw One scene, four runs · warehouse-a · 41k frames
All datasets →dock
cone
warehouse-a / run-02
night · 14k frames · cam ×2 · lidar
Ready
warehouse-a / run-03
forklift traffic · 9k frames
Processing
person
person
warehouse-a / run-04
stacked pallets · 6k frames
Needs labels
How well the models see Latest evaluation · det-1.5 vs det-1.4 on warehouse-a / run-02 (night)
All evaluations →mAP@0.5
0.74
+0.03 vs det-1.4
Recall · person
0.91
+0.06 vs det-1.4
Latency · Orin NX
23 ms
+2 ms vs det-1.4
person
0.91
+0.06
pallet
0.88
+0.01
forklift
0.79
+0.03
cone
0.72
+0.02
door
0.61
+0.09
cart
0.55
-0.03
Regression: cart −0.03 · 12 failure frames queued for labeling
Where it got it wrong
12 frames →
missed person
run-02 · low light
pallet → cart
run-04 · pallet vs cart
ghost person
run-01 · floor reflection
missed cone
run-03 · occluded by forklift