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I'm testing 3DETR in outdoor senerio (nuScenes 3D dataset), but met the same promblem as #28 . The metrics are all zero after training 90 epochs. I visualize the gt and pred boxes as follows. Also, I've found loss_cardinality is unsually high, showing that almost all queries are predicted as objects.
Ground truth:
Predictions:
Following are logs:
Epoch [0/90]; Iter [0/3150]; Loss 57.61; LR 1.00e-06; Iter time 11.74; ETA 10:16:18; Mem 25411.98MB
Epoch [0/90]; Iter [10/3150]; Loss 58.18; LR 1.68e-05; Iter time 4.36; ETA 3:48:15; Mem 25499.76MB
Epoch [0/90]; Iter [20/3150]; Loss 57.50; LR 3.27e-05; Iter time 4.03; ETA 3:30:28; Mem 25499.76MB
Epoch [0/90]; Iter [30/3150]; Loss 55.73; LR 4.85e-05; Iter time 3.45; ETA 2:59:22; Mem 25499.76MB
.......
Epoch [89/90]; Iter [3120/3150]; Loss 17.18; LR 1.11e-06; Iter time 3.35; ETA 0:01:40; Mem 25499.76MB
Epoch [89/90]; Iter [3130/3150]; Loss 17.07; LR 1.05e-06; Iter time 2.49; ETA 0:00:49; Mem 25499.76MB
Epoch [89/90]; Iter [3140/3150]; Loss 17.37; LR 1.01e-06; Iter time 2.45; ETA 0:00:24; Mem 25499.76MB
Training Finished.
====================Final Eval Numbers.
mAP0.25, mAP0.50: 0.00, 0.00
AR0.25, AR0.50: 0.00, 0.00
IOU Thresh=0.25
car Average Precision: 0.00
truck Average Precision: 0.00
trailer Average Precision: 0.00
bus Average Precision: 0.00
construction_vehicle Average Precision: 0.00
bicycle Average Precision: 0.00
motorcycle Average Precision: 0.00
pedestrian Average Precision: 0.00
traffic_cone Average Precision: 0.00
barrier Average Precision: 0.00
other Average Precision: 0.00
car Recall: 0.00
truck Recall: 0.00
trailer Recall: 0.00
bus Recall: 0.00
construction_vehicle Recall: 0.00
bicycle Recall: 0.00
motorcycle Recall: 0.00
pedestrian Recall: 0.00
traffic_cone Recall: 0.00
barrier Recall: 0.00
other Recall: 0.00
The text was updated successfully, but these errors were encountered:
fang196
changed the title
Problem with training in outdoor dataset nuScenes.
Problem with training on outdoor dataset nuScenes.
Jan 4, 2024
I'm testing 3DETR in outdoor senerio (nuScenes 3D dataset), but met the same promblem as #28 . The metrics are all zero after training 90 epochs. I visualize the gt and pred boxes as follows. Also, I've found
loss_cardinality
is unsually high, showing that almost all queries are predicted as objects.Ground truth:


Predictions:
Following are logs:
Epoch [0/90]; Iter [0/3150]; Loss 57.61; LR 1.00e-06; Iter time 11.74; ETA 10:16:18; Mem 25411.98MB
Epoch [0/90]; Iter [10/3150]; Loss 58.18; LR 1.68e-05; Iter time 4.36; ETA 3:48:15; Mem 25499.76MB
Epoch [0/90]; Iter [20/3150]; Loss 57.50; LR 3.27e-05; Iter time 4.03; ETA 3:30:28; Mem 25499.76MB
Epoch [0/90]; Iter [30/3150]; Loss 55.73; LR 4.85e-05; Iter time 3.45; ETA 2:59:22; Mem 25499.76MB
.......
Epoch [89/90]; Iter [3120/3150]; Loss 17.18; LR 1.11e-06; Iter time 3.35; ETA 0:01:40; Mem 25499.76MB
Epoch [89/90]; Iter [3130/3150]; Loss 17.07; LR 1.05e-06; Iter time 2.49; ETA 0:00:49; Mem 25499.76MB
Epoch [89/90]; Iter [3140/3150]; Loss 17.37; LR 1.01e-06; Iter time 2.45; ETA 0:00:24; Mem 25499.76MB
Training Finished.
====================Final Eval Numbers.
mAP0.25, mAP0.50: 0.00, 0.00
AR0.25, AR0.50: 0.00, 0.00
IOU Thresh=0.25
car Average Precision: 0.00
truck Average Precision: 0.00
trailer Average Precision: 0.00
bus Average Precision: 0.00
construction_vehicle Average Precision: 0.00
bicycle Average Precision: 0.00
motorcycle Average Precision: 0.00
pedestrian Average Precision: 0.00
traffic_cone Average Precision: 0.00
barrier Average Precision: 0.00
other Average Precision: 0.00
car Recall: 0.00
truck Recall: 0.00
trailer Recall: 0.00
bus Recall: 0.00
construction_vehicle Recall: 0.00
bicycle Recall: 0.00
motorcycle Recall: 0.00
pedestrian Recall: 0.00
traffic_cone Recall: 0.00
barrier Recall: 0.00
other Recall: 0.00
The text was updated successfully, but these errors were encountered: