| model type | training strategy | mAP(%) on VOC07 test | Iterations | model_name | backbone |
|---|---|---|---|---|---|
| conv5, a trous, strides=16 without ohem | 4 stages iteration as Faster RCNN | 75.77 | total steps 400k satge1 80k stage2 120k stage3 80k stage4 120k | model_A | resnet_101 |
| conv5, a trous, strides=16 without ohem | only training total_loss | 76.35 | 110k | model_B | resnet_101 |
total_loss = loss_rpn_objectness + loss_rpn_bboxes + loss_rfcn_classes + loss_rfcn_bboxes
| model_name | aeroplane | bicycle | bird | boat | bottle | bus | car | cat | chair | cow | diningtable | dog | horse | motorbike | person | pottedplant | sheep | sofa | train | tvmonitor |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| model_A | 0.8008 | 0.8004 | 0.7861 | 0.6579 | 0.4836 | 0.8646 | 0.8531 | 0.8774 | 0.6081 | 0.8517 | 0.6935 | 0.8884 | 0.8616 | 0.7821 | 0.7805 | 0.4693 | 0.7814 | 0.7742 | 0.7845 | 0.7516 |
| model_B | 0.8020 | 0.7940 | 0.7877 | 0.6402 | 0.6571 | 0.8599 | 0.8578 | 0.8736 | 0.6183 | 0.8223 | 0.6492 | 0.8728 | 0.8447 | 0.8201 | 0.7888 | 0.4607 | 0.7703 | 0.7558 | 0.8354 | 0.7596 |
model_A
momentum: 0.9
stage1 total steps 80k, init learning rate 0.001, step 60k learning rate 0.0001
stage2 total steps 120k, init learning rate 0.001, step 80k learning rate 0.0001
stage3 total steps 80k, init learning rate 0.001, step 60k learning rate 0.0001
stage4 total steps 120k, init learning rate 0.001, step 80k learning rate 0.0001
model_B
momentum: 0.9
total steps 110k, init learning rate 0.001, step 80k learning rate 0.0001
| model_name | download link | password |
|---|---|---|
| model_A | https://pan.baidu.com/s/1jIQThtW | cgwf |
| model_B | https://pan.baidu.com/s/1i4QEVRZ | v9ua |
ohem (I have tried several methods, but have no effect. The map in all the methods have dropped.)focal loss (The focal loss also have no effect.)position sensitive score map + global roi pooling class.- code refactor
- running
tools/trainval_net_rfcn.pyfile.- modify the net you want to use in line import nets, the nets provied by this project is in floder
lib/nets. - modify the
--cfgparameter which is the config file you want to use. Some config file can be find in fileexperiments/cfgs. - modify the
--weightparameter which is the pretrained model weights file. - modify the
--netwhich is the net architecture you want to use.
- modify the net you want to use in line import nets, the nets provied by this project is in floder
- some other modifies:
- you can modify the
loss functionas you requirement in line network.py#L646 - you can modify the
RFCNarchitecture in line resnet_v1_rfcn_hole.py#L279_ - you'd better using the
resnet_v1_rfcn_hole.pyandnetwork.pyfile.
- you can modify the
3 R-FCN: Object Detection via Region-based Fully Convolutional Networks
4 An Implementation of Faster RCNN with Study for Region Sampling