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auroua / tf_rfcn

TensorFlow RFCN ver 0.1
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object-detection tensorflow

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RFCN_TensorFlow

Results
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

Result Details
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
Training Details

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 Download Links
model_name download link password
model_A https://pan.baidu.com/s/1jIQThtW cgwf
model_B https://pan.baidu.com/s/1i4QEVRZ v9ua
Tasks
Training Pipline
  1. running tools/trainval_net_rfcn.py file.
    • modify the net you want to use in line import nets, the nets provied by this project is in floder lib/nets.
    • modify the --cfg parameter which is the config file you want to use. Some config file can be find in file experiments/cfgs.
    • modify the --weight parameter which is the pretrained model weights file.
    • modify the --net which is the net architecture you want to use.
  2. some other modifies:
    • you can modify the loss function as you requirement in line network.py#L646
    • you can modify the RFCN architecture in line resnet_v1_rfcn_hole.py#L279_
    • you'd better using the resnet_v1_rfcn_hole.py and network.py file.
References:

1 tf-faster-rcnn

2 tensorflow-object-detection

3 R-FCN: Object Detection via Region-based Fully Convolutional Networks

4 An Implementation of Faster RCNN with Study for Region Sampling