I am using a pre trained pytorch object detection model for transfer learning on custom data I want to train.
I am referring to Building your own object detector — PyTorch vs TensorFlow and how to even get started?
My custom data I want to train is being detected as label 77
-Cell Phone
when inferred on fasterrcnn_resnet50_fpn
, so I replaced the final classification layers weight
& bias
with only 0
label background
and 77
label Cell Phone
I started with training
First I used Adam
with LambdLR
Optimizer
optimizer = torch.optim.Adam(params,lr=0.005,betas=(0.9,0.999),eps=1e-08,weight_decay=0.0005,amsgrad=False)
Learning rate
lambda1 = lambda epoch: 0.65 ** epoch
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)
This is it’s training output
Epoch: [0] [188/189] eta: 0:00:05 lr: 0.005000 loss: 0.1684 (0.1853) loss_classifier: 0.0370 (0.0534) loss_box_reg: 0.1215 (0.1199) loss_objectness: 0.0060 (0.0063) loss_rpn_box_reg: 0.0054 (0.0056) time: 5.4183 data: 0.0438 max mem: 8036
Epoch: [0] Total time: 0:16:33 (5.2578 s / it)
Test: Total time: 0:02:22 (0.3780 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.062
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.166
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.039
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.063
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.104
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.329
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.329
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.329
Epoch: [1] [188/189] eta: 0:00:05 lr: 0.003250 loss: 0.1665 (0.1679) loss_classifier: 0.0327 (0.0352) loss_box_reg: 0.1244 (0.1207) loss_objectness: 0.0067 (0.0065) loss_rpn_box_reg: 0.0047 (0.0056) time: 4.8139 data: 0.0454 max mem: 8036
Epoch: [1] Total time: 0:16:07 (5.1194 s / it)
Test: Total time: 0:02:23 (0.3783 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.058
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.161
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.033
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.060
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.100
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.337
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.337
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.337
Epoch: [2] [188/189] eta: 0:00:05 lr: 0.002113 loss: 0.1724 (0.1649) loss_classifier: 0.0333 (0.0328) loss_box_reg: 0.1212 (0.1199) loss_objectness: 0.0065 (0.0066) loss_rpn_box_reg: 0.0054 (0.0055) time: 4.6630 data: 0.0454 max mem: 8036
Epoch: [2] Total time: 0:16:22 (5.1970 s / it)
Test: Total time: 0:02:22 (0.3779 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.063
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.171
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.038
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.064
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.104
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.348
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.348
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.348
Epoch: [3] [188/189] eta: 0:00:05 lr: 0.001373 loss: 0.1601 (0.1640) loss_classifier: 0.0314 (0.0322) loss_box_reg: 0.1156 (0.1199) loss_objectness: 0.0052 (0.0062) loss_rpn_box_reg: 0.0052 (0.0056) time: 5.1234 data: 0.0467 max mem: 8036
Epoch: [3] Total time: 0:16:28 (5.2321 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.061
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.166
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.035
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.101
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.346
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.346
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.346
Epoch: [4] [188/189] eta: 0:00:05 lr: 0.000893 loss: 0.1527 (0.1635) loss_classifier: 0.0286 (0.0314) loss_box_reg: 0.1091 (0.1204) loss_objectness: 0.0054 (0.0062) loss_rpn_box_reg: 0.0051 (0.0056) time: 5.1214 data: 0.0482 max mem: 8036
Epoch: [4] Total time: 0:16:28 (5.2280 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.061
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.170
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.035
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.102
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.346
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.346
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.346
Epoch: [5] [188/189] eta: 0:00:05 lr: 0.000580 loss: 0.1585 (0.1635) loss_classifier: 0.0271 (0.0312) loss_box_reg: 0.1103 (0.1200) loss_objectness: 0.0061 (0.0067) loss_rpn_box_reg: 0.0059 (0.0055) time: 5.1237 data: 0.0476 max mem: 8036
Epoch: [5] Total time: 0:16:23 (5.2050 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.061
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.169
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.035
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.102
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.349
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.349
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.349
Epoch: [6] [188/189] eta: 0:00:05 lr: 0.000377 loss: 0.1621 (0.1628) loss_classifier: 0.0308 (0.0311) loss_box_reg: 0.1170 (0.1197) loss_objectness: 0.0061 (0.0065) loss_rpn_box_reg: 0.0052 (0.0055) time: 5.2761 data: 0.0485 max mem: 8036
Epoch: [6] Total time: 0:16:32 (5.2489 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.061
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.170
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.035
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.102
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.352
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.352
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.352
Epoch: [7] [188/189] eta: 0:00:05 lr: 0.000245 loss: 0.1600 (0.1630) loss_classifier: 0.0294 (0.0309) loss_box_reg: 0.1154 (0.1201) loss_objectness: 0.0064 (0.0064) loss_rpn_box_reg: 0.0050 (0.0056) time: 5.5080 data: 0.0453 max mem: 8036
Epoch: [7] Total time: 0:16:51 (5.3507 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.061
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.170
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.033
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.063
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.102
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.349
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.349
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.349
Epoch: [8] [188/189] eta: 0:00:05 lr: 0.000159 loss: 0.1602 (0.1624) loss_classifier: 0.0283 (0.0308) loss_box_reg: 0.1206 (0.1198) loss_objectness: 0.0051 (0.0062) loss_rpn_box_reg: 0.0047 (0.0056) time: 5.4158 data: 0.0434 max mem: 8036
Epoch: [8] Total time: 0:16:42 (5.3026 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.061
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.169
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.035
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.063
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.102
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.350
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.350
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.350
Epoch: [9] [188/189] eta: 0:00:05 lr: 0.000104 loss: 0.1595 (0.1623) loss_classifier: 0.0278 (0.0308) loss_box_reg: 0.1183 (0.1195) loss_objectness: 0.0053 (0.0064) loss_rpn_box_reg: 0.0051 (0.0056) time: 4.9586 data: 0.0437 max mem: 8036
Epoch: [9] Total time: 0:16:14 (5.1535 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.062
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.170
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.035
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.063
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.102
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.351
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.351
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.351
I don’t know why I get -1 values, what does -1 mean?
Can anyone guide me as to how to improve training after looking at these results
I feel I need to used stepLR
instead of LambdLR
to bring down losses an improve accuracy
Second I used SGD
with StepLR
Optimizer
optimizer = torch.optim.SGD(params, lr=0.005,momentum=0.9, weight_decay=0.0005)
Learning Rate
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,step_size=3,gamma=0.1)
This is it’s training output
Epoch: [0] [188/189] eta: 0:00:01 lr: 0.005000 loss: 0.1822 (0.2051) loss_classifier: 0.0509 (0.0732) loss_box_reg: 0.1215 (0.1199) loss_objectness: 0.0063 (0.0064) loss_rpn_box_reg: 0.0054 (0.0056) time: 1.7622 data: 0.0290 max mem: 10953
Epoch: [0] Total time: 0:05:29 (1.7410 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.062
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.171
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.033
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.063
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.098
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.342
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.342
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.
Epoch: [1] [188/189] eta: 0:00:01 lr: 0.005000 loss: 0.1809 (0.1800) loss_classifier: 0.0421 (0.0472) loss_box_reg: 0.1244 (0.1207) loss_objectness: 0.0066 (0.0066) loss_rpn_box_reg: 0.0047 (0.0056) time: 1.6265 data: 0.0287 max mem: 10953
Epoch: [1] Total time: 0:05:21 (1.7014 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.062
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.168
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.033
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.063
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.103
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.346
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.346
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.346
Epoch: [2] [188/189] eta: 0:00:01 lr: 0.005000 loss: 0.1827 (0.1752) loss_classifier: 0.0415 (0.0432) loss_box_reg: 0.1212 (0.1199) loss_objectness: 0.0079 (0.0066) loss_rpn_box_reg: 0.0054 (0.0055) time: 1.5905 data: 0.0288 max mem: 10953
Epoch: [2] Total time: 0:05:24 (1.7192 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.061
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.167
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.033
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.105
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.343
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.343
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.343
Epoch: [3] [188/189] eta: 0:00:01 lr: 0.000500 loss: 0.1736 (0.1735) loss_classifier: 0.0423 (0.0418) loss_box_reg: 0.1156 (0.1199) loss_objectness: 0.0053 (0.0062) loss_rpn_box_reg: 0.0052 (0.0056) time: 1.6961 data: 0.0297 max mem: 10953
Epoch: [3] Total time: 0:05:26 (1.7274 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.061
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.167
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.034
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.105
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.343
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.343
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.343
Epoch: [4] [188/189] eta: 0:00:01 lr: 0.000500 loss: 0.1579 (0.1737) loss_classifier: 0.0364 (0.0414) loss_box_reg: 0.1091 (0.1204) loss_objectness: 0.0052 (0.0064) loss_rpn_box_reg: 0.0051 (0.0056) time: 1.6901 data: 0.0277 max mem: 10953
Epoch: [4] Total time: 0:05:26 (1.7270 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.061
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.172
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.033
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.106
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.344
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.344
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.344
Epoch: [5] [188/189] eta: 0:00:01 lr: 0.000500 loss: 0.1687 (0.1740) loss_classifier: 0.0368 (0.0419) loss_box_reg: 0.1103 (0.1200) loss_objectness: 0.0048 (0.0065) loss_rpn_box_reg: 0.0059 (0.0055) time: 1.6966 data: 0.0297 max mem: 10953
Epoch: [5] Total time: 0:05:25 (1.7211 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.061
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.170
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.033
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.106
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.343
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.343
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.343
Epoch: [6] [188/189] eta: 0:00:01 lr: 0.000050 loss: 0.1714 (0.1729) loss_classifier: 0.0423 (0.0415) loss_box_reg: 0.1170 (0.1197) loss_objectness: 0.0046 (0.0062) loss_rpn_box_reg: 0.0052 (0.0055) time: 1.7284 data: 0.0293 max mem: 10953
Epoch: [6] Total time: 0:05:26 (1.7288 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.061
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.170
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.033
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.106
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.343
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.343
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.343
Epoch: [7] [188/189] eta: 0:00:01 lr: 0.000050 loss: 0.1639 (0.1735) loss_classifier: 0.0378 (0.0415) loss_box_reg: 0.1154 (0.1201) loss_objectness: 0.0048 (0.0063) loss_rpn_box_reg: 0.0050 (0.0056) time: 1.7644 data: 0.0288 max mem: 10953
Epoch: [7] Total time: 0:05:30 (1.7465 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.061
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.170
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.033
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.106
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.343
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.343
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.343
Epoch: [8] [188/189] eta: 0:00:01 lr: 0.000050 loss: 0.1732 (0.1731) loss_classifier: 0.0375 (0.0414) loss_box_reg: 0.1206 (0.1198) loss_objectness: 0.0055 (0.0063) loss_rpn_box_reg: 0.0047 (0.0056) time: 1.7641 data: 0.0280 max mem: 10953
Epoch: [8] Total time: 0:05:29 (1.7419 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.061
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.170
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.033
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.106
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.343
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.343
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.343
Epoch: [9] [188/189] eta: 0:00:01 lr: 0.000005 loss: 0.1699 (0.1729) loss_classifier: 0.0380 (0.0414) loss_box_reg: 0.1183 (0.1195) loss_objectness: 0.0057 (0.0065) loss_rpn_box_reg: 0.0051 (0.0056) time: 1.6622 data: 0.0298 max mem: 10953
Epoch: [9] Total time: 0:05:23 (1.7121 s / it)
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.061
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.170
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.033
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.106
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.343
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.343
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.343