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I'm following this tutorial to fine tune Faster RCNN model, during training process a lot of statistics are produced however I don't know how to interpret them.

what are major characteristics to look at ? How to characterize my model performance ?

Here is an example of output.

Epoch: [6]  [ 10/119]  eta: 0:01:13  lr: 0.000050  loss: 0.4129 (0.4104)  loss_classifier: 0.1277 (0.1263)  loss_box_reg: 0.2164 (0.2059)  loss_objectness: 0.0244 (0.0309)  loss_rpn_box_reg: 0.0487 (0.0473)  time: 0.6770  data: 0.1253  max mem: 3105
Epoch: [6]  [ 20/119]  eta: 0:01:07  lr: 0.000050  loss: 0.4165 (0.4302)  loss_classifier: 0.1277 (0.1290)  loss_box_reg: 0.2180 (0.2136)  loss_objectness: 0.0353 (0.0385)  loss_rpn_box_reg: 0.0499 (0.0491)  time: 0.6843  data: 0.1265  max mem: 3105
Epoch: [6]  [ 30/119]  eta: 0:01:00  lr: 0.000050  loss: 0.4205 (0.4228)  loss_classifier: 0.1271 (0.1277)  loss_box_reg: 0.2125 (0.2093)  loss_objectness: 0.0334 (0.0374)  loss_rpn_box_reg: 0.0499 (0.0484)  time: 0.6819  data: 0.1274  max mem: 3105
Epoch: [6]  [ 40/119]  eta: 0:00:53  lr: 0.000050  loss: 0.4127 (0.4205)  loss_classifier: 0.1209 (0.1265)  loss_box_reg: 0.2102 (0.2085)  loss_objectness: 0.0315 (0.0376)  loss_rpn_box_reg: 0.0475 (0.0479)  time: 0.6748  data: 0.1282  max mem: 3105
Epoch: [6]  [ 50/119]  eta: 0:00:46  lr: 0.000050  loss: 0.3973 (0.4123)  loss_classifier: 0.1202 (0.1248)  loss_box_reg: 0.1947 (0.2039)  loss_objectness: 0.0315 (0.0366)  loss_rpn_box_reg: 0.0459 (0.0470)  time: 0.6730  data: 0.1297  max mem: 3105
Epoch: [6]  [ 60/119]  eta: 0:00:39  lr: 0.000050  loss: 0.3900 (0.4109)  loss_classifier: 0.1206 (0.1248)  loss_box_reg: 0.1876 (0.2030)  loss_objectness: 0.0345 (0.0365)  loss_rpn_box_reg: 0.0431 (0.0467)  time: 0.6692  data: 0.1276  max mem: 3105
Epoch: [6]  [ 70/119]  eta: 0:00:33  lr: 0.000050  loss: 0.3984 (0.4085)  loss_classifier: 0.1172 (0.1242)  loss_box_reg: 0.2069 (0.2024)  loss_objectness: 0.0328 (0.0354)  loss_rpn_box_reg: 0.0458 (0.0464)  time: 0.6707  data: 0.1252  max mem: 3105
Epoch: [6]  [ 80/119]  eta: 0:00:26  lr: 0.000050  loss: 0.4153 (0.4113)  loss_classifier: 0.1178 (0.1246)  loss_box_reg: 0.2123 (0.2036)  loss_objectness: 0.0328 (0.0364)  loss_rpn_box_reg: 0.0480 (0.0468)  time: 0.6744  data: 0.1264  max mem: 3105
Epoch: [6]  [ 90/119]  eta: 0:00:19  lr: 0.000050  loss: 0.4294 (0.4107)  loss_classifier: 0.1178 (0.1238)  loss_box_reg: 0.2098 (0.2021)  loss_objectness: 0.0418 (0.0381)  loss_rpn_box_reg: 0.0495 (0.0466)  time: 0.6856  data: 0.1302  max mem: 3105
Epoch: [6]  [100/119]  eta: 0:00:12  lr: 0.000050  loss: 0.4295 (0.4135)  loss_classifier: 0.1171 (0.1235)  loss_box_reg: 0.2124 (0.2034)  loss_objectness: 0.0460 (0.0397)  loss_rpn_box_reg: 0.0498 (0.0469)  time: 0.6955  data: 0.1345  max mem: 3105
Epoch: [6]  [110/119]  eta: 0:00:06  lr: 0.000050  loss: 0.4126 (0.4117)  loss_classifier: 0.1229 (0.1233)  loss_box_reg: 0.2119 (0.2024)  loss_objectness: 0.0430 (0.0394)  loss_rpn_box_reg: 0.0481 (0.0466)  time: 0.6822  data: 0.1306  max mem: 3105
Epoch: [6]  [118/119]  eta: 0:00:00  lr: 0.000050  loss: 0.4006 (0.4113)  loss_classifier: 0.1171 (0.1227)  loss_box_reg: 0.2028 (0.2028)  loss_objectness: 0.0366 (0.0391)  loss_rpn_box_reg: 0.0481 (0.0466)  time: 0.6583  data: 0.1230  max mem: 3105
Epoch: [6] Total time: 0:01:20 (0.6760 s / it)
creating index...
index created!
Test:  [ 0/59]  eta: 0:00:15  model_time: 0.1188 (0.1188)  evaluator_time: 0.0697 (0.0697)  time: 0.2561  data: 0.0634  max mem: 3105
Test:  [58/59]  eta: 0:00:00  model_time: 0.1086 (0.1092)  evaluator_time: 0.0439 (0.0607)  time: 0.2361  data: 0.0629  max mem: 3105
Test: Total time: 0:00:14 (0.2378 s / it)
Averaged stats: model_time: 0.1086 (0.1092)  evaluator_time: 0.0439 (0.0607)
Accumulating evaluation results...
DONE (t=0.02s).
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.210
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.643
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.079
 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 ] = 0.210
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.011
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.096
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.333
 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 ] = 0.333
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Epoch: [7]  [  0/119]  eta: 0:01:16  lr: 0.000050  loss: 0.3851 (0.3851)  loss_classifier: 0.1334 (0.1334)  loss_box_reg: 0.1845 (0.1845)  loss_objectness: 0.0287 (0.0287)  loss_rpn_box_reg: 0.0385 (0.0385)  time: 0.6433  data: 0.1150  max mem: 3105
Epoch: [7]  [ 10/119]  eta: 0:01:12  lr: 0.000050  loss: 0.3997 (0.4045)  loss_classifier: 0.1250 (0.1259)  loss_box_reg: 0.1973 (0.2023)  loss_objectness: 0.0292 (0.0303)  loss_rpn_box_reg: 0.0479 (0.0459)  time: 0.6692  data: 0.1252  max mem: 3105
Epoch: [7]  [ 20/119]  eta: 0:01:07  lr: 0.000050  loss: 0.4224 (0.4219)  loss_classifier: 0.1250 (0.1262)  loss_box_reg: 0.2143 (0.2101)  loss_objectness: 0.0333 (0.0373)  loss_rpn_box_reg: 0.0493 (0.0484)  time: 0.6809  data: 0.1286  max mem: 3105
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Region Proposal Network is a subcomponent of the Fast RCNN and Faster RCNN architectures. It proposes candidate boxes and scores whether there is an object in this regions. RPN loss and objectness loss must be losses of this predictions.

Regressor loss is the loss of the prediction of bounding box coordinates, and classifier loss is the loss of prediction of object classes in bounding boxes.

IOU is acronym for intersection over union, and it gives how much bounding boxes are overlapped. In RPN it is calculated between suggested boxes(anchors), and ground truths. Higher IOU scores means suggested box includes an object of interest.

Average precision is the average of the areas under the precision recall curve for each of the object classes.

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