search

opencv / opencv

Open Source Computer Vision Library
https://opencv.org
Apache License 2.0
79.32k stars 55.85k forks source link

Trained RFCN model can't detect objects with opencv 3.4.1 API #12607

Closed ivshli closed 6 years ago

ivshli commented 6 years ago

Detailed description

I trained a new RFCN model (ResNet-50 with pascal format data sets) using py-R-FCN project with customized data, new model detect only 2 classes (background and new class) and works very well in python env with "demo_rfcn.py " I changed anchor scale and ratio values in order to detect my object (a small size object in image) In order to running the trained model with Opencv, I adapted provided "rfcn_pascal_voc_resnet50.prototxt" with my own "test_angostic.prototxt" (modifiy some num_output etc) but when I run opencv to test on some images, nothing was detected.

The model is trained with input shape = 300x300. I don't know if there is somethings to modify in DetectionOutput layer or proposal_reshape layer etc Any ideas? Thanks a lot

Steps to reproduce

I will attach my test.prototxt here if that can help

test.prototxt ```yaml name: "ResNet50" input: "data" input_shape { dim: 1 dim: 3 dim: 300 dim: 300 } input: "im_info" input_shape { dim: 1 dim: 3 } layer { bottom: "data" top: "conv1" name: "conv1" type: "Convolution" convolution_param { num_output: 64 kernel_size: 7 pad: 3 stride: 2 } param { lr_mult: 0.0 } param { lr_mult: 0.0 } } layer { bottom: "conv1" top: "conv1" name: "bn_conv1" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "conv1" top: "conv1" name: "scale_conv1" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "conv1" top: "conv1" name: "conv1_relu" type: "ReLU" } layer { bottom: "conv1" top: "pool1" name: "pool1" type: "Pooling" pooling_param { kernel_size: 3 stride: 2 pool: MAX } } layer { bottom: "pool1" top: "res2a_branch1" name: "res2a_branch1" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 0.0 } } layer { bottom: "res2a_branch1" top: "res2a_branch1" name: "bn2a_branch1" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2a_branch1" top: "res2a_branch1" name: "scale2a_branch1" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "pool1" top: "res2a_branch2a" name: "res2a_branch2a" type: "Convolution" convolution_param { num_output: 64 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 0.0 } } layer { bottom: "res2a_branch2a" top: "res2a_branch2a" name: "bn2a_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2a_branch2a" top: "res2a_branch2a" name: "scale2a_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2a_branch2a" top: "res2a_branch2a" name: "res2a_branch2a_relu" type: "ReLU" } layer { bottom: "res2a_branch2a" top: "res2a_branch2b" name: "res2a_branch2b" type: "Convolution" convolution_param { num_output: 64 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 0.0 } } layer { bottom: "res2a_branch2b" top: "res2a_branch2b" name: "bn2a_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2a_branch2b" top: "res2a_branch2b" name: "scale2a_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2a_branch2b" top: "res2a_branch2b" name: "res2a_branch2b_relu" type: "ReLU" } layer { bottom: "res2a_branch2b" top: "res2a_branch2c" name: "res2a_branch2c" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 0.0 } } layer { bottom: "res2a_branch2c" top: "res2a_branch2c" name: "bn2a_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2a_branch2c" top: "res2a_branch2c" name: "scale2a_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2a_branch1" bottom: "res2a_branch2c" top: "res2a" name: "res2a" type: "Eltwise" } layer { bottom: "res2a" top: "res2a" name: "res2a_relu" type: "ReLU" } layer { bottom: "res2a" top: "res2b_branch2a" name: "res2b_branch2a" type: "Convolution" convolution_param { num_output: 64 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 0.0 } } layer { bottom: "res2b_branch2a" top: "res2b_branch2a" name: "bn2b_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2b_branch2a" top: "res2b_branch2a" name: "scale2b_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2b_branch2a" top: "res2b_branch2a" name: "res2b_branch2a_relu" type: "ReLU" } layer { bottom: "res2b_branch2a" top: "res2b_branch2b" name: "res2b_branch2b" type: "Convolution" convolution_param { num_output: 64 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 0.0 } } layer { bottom: "res2b_branch2b" top: "res2b_branch2b" name: "bn2b_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2b_branch2b" top: "res2b_branch2b" name: "scale2b_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2b_branch2b" top: "res2b_branch2b" name: "res2b_branch2b_relu" type: "ReLU" } layer { bottom: "res2b_branch2b" top: "res2b_branch2c" name: "res2b_branch2c" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 0.0 } } layer { bottom: "res2b_branch2c" top: "res2b_branch2c" name: "bn2b_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2b_branch2c" top: "res2b_branch2c" name: "scale2b_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2a" bottom: "res2b_branch2c" top: "res2b" name: "res2b" type: "Eltwise" } layer { bottom: "res2b" top: "res2b" name: "res2b_relu" type: "ReLU" } layer { bottom: "res2b" top: "res2c_branch2a" name: "res2c_branch2a" type: "Convolution" convolution_param { num_output: 64 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 0.0 } } layer { bottom: "res2c_branch2a" top: "res2c_branch2a" name: "bn2c_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2c_branch2a" top: "res2c_branch2a" name: "scale2c_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2c_branch2a" top: "res2c_branch2a" name: "res2c_branch2a_relu" type: "ReLU" } layer { bottom: "res2c_branch2a" top: "res2c_branch2b" name: "res2c_branch2b" type: "Convolution" convolution_param { num_output: 64 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 0.0 } } layer { bottom: "res2c_branch2b" top: "res2c_branch2b" name: "bn2c_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2c_branch2b" top: "res2c_branch2b" name: "scale2c_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2c_branch2b" top: "res2c_branch2b" name: "res2c_branch2b_relu" type: "ReLU" } layer { bottom: "res2c_branch2b" top: "res2c_branch2c" name: "res2c_branch2c" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 0.0 } } layer { bottom: "res2c_branch2c" top: "res2c_branch2c" name: "bn2c_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2c_branch2c" top: "res2c_branch2c" name: "scale2c_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2b" bottom: "res2c_branch2c" top: "res2c" name: "res2c" type: "Eltwise" } layer { bottom: "res2c" top: "res2c" name: "res2c_relu" type: "ReLU" } layer { bottom: "res2c" top: "res3a_branch1" name: "res3a_branch1" type: "Convolution" convolution_param { num_output: 512 kernel_size: 1 pad: 0 stride: 2 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res3a_branch1" top: "res3a_branch1" name: "bn3a_branch1" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3a_branch1" top: "res3a_branch1" name: "scale3a_branch1" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res2c" top: "res3a_branch2a" name: "res3a_branch2a" type: "Convolution" convolution_param { num_output: 128 kernel_size: 1 pad: 0 stride: 2 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res3a_branch2a" top: "res3a_branch2a" name: "bn3a_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3a_branch2a" top: "res3a_branch2a" name: "scale3a_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3a_branch2a" top: "res3a_branch2a" name: "res3a_branch2a_relu" type: "ReLU" } layer { bottom: "res3a_branch2a" top: "res3a_branch2b" name: "res3a_branch2b" type: "Convolution" convolution_param { num_output: 128 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res3a_branch2b" top: "res3a_branch2b" name: "bn3a_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3a_branch2b" top: "res3a_branch2b" name: "scale3a_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3a_branch2b" top: "res3a_branch2b" name: "res3a_branch2b_relu" type: "ReLU" } layer { bottom: "res3a_branch2b" top: "res3a_branch2c" name: "res3a_branch2c" type: "Convolution" convolution_param { num_output: 512 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res3a_branch2c" top: "res3a_branch2c" name: "bn3a_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3a_branch2c" top: "res3a_branch2c" name: "scale3a_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3a_branch1" bottom: "res3a_branch2c" top: "res3a" name: "res3a" type: "Eltwise" } layer { bottom: "res3a" top: "res3a" name: "res3a_relu" type: "ReLU" } layer { bottom: "res3a" top: "res3b_branch2a" name: "res3b_branch2a" type: "Convolution" convolution_param { num_output: 128 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res3b_branch2a" top: "res3b_branch2a" name: "bn3b_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3b_branch2a" top: "res3b_branch2a" name: "scale3b_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3b_branch2a" top: "res3b_branch2a" name: "res3b_branch2a_relu" type: "ReLU" } layer { bottom: "res3b_branch2a" top: "res3b_branch2b" name: "res3b_branch2b" type: "Convolution" convolution_param { num_output: 128 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res3b_branch2b" top: "res3b_branch2b" name: "bn3b_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3b_branch2b" top: "res3b_branch2b" name: "scale3b_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3b_branch2b" top: "res3b_branch2b" name: "res3b_branch2b_relu" type: "ReLU" } layer { bottom: "res3b_branch2b" top: "res3b_branch2c" name: "res3b_branch2c" type: "Convolution" convolution_param { num_output: 512 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res3b_branch2c" top: "res3b_branch2c" name: "bn3b_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3b_branch2c" top: "res3b_branch2c" name: "scale3b_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3a" bottom: "res3b_branch2c" top: "res3b" name: "res3b" type: "Eltwise" } layer { bottom: "res3b" top: "res3b" name: "res3b_relu" type: "ReLU" } layer { bottom: "res3b" top: "res3c_branch2a" name: "res3c_branch2a" type: "Convolution" convolution_param { num_output: 128 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res3c_branch2a" top: "res3c_branch2a" name: "bn3c_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3c_branch2a" top: "res3c_branch2a" name: "scale3c_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3c_branch2a" top: "res3c_branch2a" name: "res3c_branch2a_relu" type: "ReLU" } layer { bottom: "res3c_branch2a" top: "res3c_branch2b" name: "res3c_branch2b" type: "Convolution" convolution_param { num_output: 128 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res3c_branch2b" top: "res3c_branch2b" name: "bn3c_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3c_branch2b" top: "res3c_branch2b" name: "scale3c_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3c_branch2b" top: "res3c_branch2b" name: "res3c_branch2b_relu" type: "ReLU" } layer { bottom: "res3c_branch2b" top: "res3c_branch2c" name: "res3c_branch2c" type: "Convolution" convolution_param { num_output: 512 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res3c_branch2c" top: "res3c_branch2c" name: "bn3c_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3c_branch2c" top: "res3c_branch2c" name: "scale3c_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3b" bottom: "res3c_branch2c" top: "res3c" name: "res3c" type: "Eltwise" } layer { bottom: "res3c" top: "res3c" name: "res3c_relu" type: "ReLU" } layer { bottom: "res3c" top: "res3d_branch2a" name: "res3d_branch2a" type: "Convolution" convolution_param { num_output: 128 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res3d_branch2a" top: "res3d_branch2a" name: "bn3d_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3d_branch2a" top: "res3d_branch2a" name: "scale3d_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3d_branch2a" top: "res3d_branch2a" name: "res3d_branch2a_relu" type: "ReLU" } layer { bottom: "res3d_branch2a" top: "res3d_branch2b" name: "res3d_branch2b" type: "Convolution" convolution_param { num_output: 128 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res3d_branch2b" top: "res3d_branch2b" name: "bn3d_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3d_branch2b" top: "res3d_branch2b" name: "scale3d_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3d_branch2b" top: "res3d_branch2b" name: "res3d_branch2b_relu" type: "ReLU" } layer { bottom: "res3d_branch2b" top: "res3d_branch2c" name: "res3d_branch2c" type: "Convolution" convolution_param { num_output: 512 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res3d_branch2c" top: "res3d_branch2c" name: "bn3d_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3d_branch2c" top: "res3d_branch2c" name: "scale3d_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3c" bottom: "res3d_branch2c" top: "res3d" name: "res3d" type: "Eltwise" } layer { bottom: "res3d" top: "res3d" name: "res3d_relu" type: "ReLU" } layer { bottom: "res3d" top: "res4a_branch1" name: "res4a_branch1" type: "Convolution" convolution_param { num_output: 1024 kernel_size: 1 pad: 0 stride: 2 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4a_branch1" top: "res4a_branch1" name: "bn4a_branch1" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4a_branch1" top: "res4a_branch1" name: "scale4a_branch1" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res3d" top: "res4a_branch2a" name: "res4a_branch2a" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 2 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4a_branch2a" top: "res4a_branch2a" name: "bn4a_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4a_branch2a" top: "res4a_branch2a" name: "scale4a_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4a_branch2a" top: "res4a_branch2a" name: "res4a_branch2a_relu" type: "ReLU" } layer { bottom: "res4a_branch2a" top: "res4a_branch2b" name: "res4a_branch2b" type: "Convolution" convolution_param { num_output: 256 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4a_branch2b" top: "res4a_branch2b" name: "bn4a_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4a_branch2b" top: "res4a_branch2b" name: "scale4a_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4a_branch2b" top: "res4a_branch2b" name: "res4a_branch2b_relu" type: "ReLU" } layer { bottom: "res4a_branch2b" top: "res4a_branch2c" name: "res4a_branch2c" type: "Convolution" convolution_param { num_output: 1024 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4a_branch2c" top: "res4a_branch2c" name: "bn4a_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4a_branch2c" top: "res4a_branch2c" name: "scale4a_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4a_branch1" bottom: "res4a_branch2c" top: "res4a" name: "res4a" type: "Eltwise" } layer { bottom: "res4a" top: "res4a" name: "res4a_relu" type: "ReLU" } layer { bottom: "res4a" top: "res4b_branch2a" name: "res4b_branch2a" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4b_branch2a" top: "res4b_branch2a" name: "bn4b_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4b_branch2a" top: "res4b_branch2a" name: "scale4b_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4b_branch2a" top: "res4b_branch2a" name: "res4b_branch2a_relu" type: "ReLU" } layer { bottom: "res4b_branch2a" top: "res4b_branch2b" name: "res4b_branch2b" type: "Convolution" convolution_param { num_output: 256 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4b_branch2b" top: "res4b_branch2b" name: "bn4b_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4b_branch2b" top: "res4b_branch2b" name: "scale4b_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4b_branch2b" top: "res4b_branch2b" name: "res4b_branch2b_relu" type: "ReLU" } layer { bottom: "res4b_branch2b" top: "res4b_branch2c" name: "res4b_branch2c" type: "Convolution" convolution_param { num_output: 1024 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4b_branch2c" top: "res4b_branch2c" name: "bn4b_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4b_branch2c" top: "res4b_branch2c" name: "scale4b_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4a" bottom: "res4b_branch2c" top: "res4b" name: "res4b" type: "Eltwise" } layer { bottom: "res4b" top: "res4b" name: "res4b_relu" type: "ReLU" } layer { bottom: "res4b" top: "res4c_branch2a" name: "res4c_branch2a" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4c_branch2a" top: "res4c_branch2a" name: "bn4c_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4c_branch2a" top: "res4c_branch2a" name: "scale4c_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4c_branch2a" top: "res4c_branch2a" name: "res4c_branch2a_relu" type: "ReLU" } layer { bottom: "res4c_branch2a" top: "res4c_branch2b" name: "res4c_branch2b" type: "Convolution" convolution_param { num_output: 256 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4c_branch2b" top: "res4c_branch2b" name: "bn4c_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4c_branch2b" top: "res4c_branch2b" name: "scale4c_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4c_branch2b" top: "res4c_branch2b" name: "res4c_branch2b_relu" type: "ReLU" } layer { bottom: "res4c_branch2b" top: "res4c_branch2c" name: "res4c_branch2c" type: "Convolution" convolution_param { num_output: 1024 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4c_branch2c" top: "res4c_branch2c" name: "bn4c_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4c_branch2c" top: "res4c_branch2c" name: "scale4c_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4b" bottom: "res4c_branch2c" top: "res4c" name: "res4c" type: "Eltwise" } layer { bottom: "res4c" top: "res4c" name: "res4c_relu" type: "ReLU" } layer { bottom: "res4c" top: "res4d_branch2a" name: "res4d_branch2a" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4d_branch2a" top: "res4d_branch2a" name: "bn4d_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4d_branch2a" top: "res4d_branch2a" name: "scale4d_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4d_branch2a" top: "res4d_branch2a" name: "res4d_branch2a_relu" type: "ReLU" } layer { bottom: "res4d_branch2a" top: "res4d_branch2b" name: "res4d_branch2b" type: "Convolution" convolution_param { num_output: 256 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4d_branch2b" top: "res4d_branch2b" name: "bn4d_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4d_branch2b" top: "res4d_branch2b" name: "scale4d_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4d_branch2b" top: "res4d_branch2b" name: "res4d_branch2b_relu" type: "ReLU" } layer { bottom: "res4d_branch2b" top: "res4d_branch2c" name: "res4d_branch2c" type: "Convolution" convolution_param { num_output: 1024 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4d_branch2c" top: "res4d_branch2c" name: "bn4d_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4d_branch2c" top: "res4d_branch2c" name: "scale4d_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4c" bottom: "res4d_branch2c" top: "res4d" name: "res4d" type: "Eltwise" } layer { bottom: "res4d" top: "res4d" name: "res4d_relu" type: "ReLU" } layer { bottom: "res4d" top: "res4e_branch2a" name: "res4e_branch2a" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4e_branch2a" top: "res4e_branch2a" name: "bn4e_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4e_branch2a" top: "res4e_branch2a" name: "scale4e_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4e_branch2a" top: "res4e_branch2a" name: "res4e_branch2a_relu" type: "ReLU" } layer { bottom: "res4e_branch2a" top: "res4e_branch2b" name: "res4e_branch2b" type: "Convolution" convolution_param { num_output: 256 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4e_branch2b" top: "res4e_branch2b" name: "bn4e_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4e_branch2b" top: "res4e_branch2b" name: "scale4e_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4e_branch2b" top: "res4e_branch2b" name: "res4e_branch2b_relu" type: "ReLU" } layer { bottom: "res4e_branch2b" top: "res4e_branch2c" name: "res4e_branch2c" type: "Convolution" convolution_param { num_output: 1024 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4e_branch2c" top: "res4e_branch2c" name: "bn4e_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4e_branch2c" top: "res4e_branch2c" name: "scale4e_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4d" bottom: "res4e_branch2c" top: "res4e" name: "res4e" type: "Eltwise" } layer { bottom: "res4e" top: "res4e" name: "res4e_relu" type: "ReLU" } layer { bottom: "res4e" top: "res4f_branch2a" name: "res4f_branch2a" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4f_branch2a" top: "res4f_branch2a" name: "bn4f_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4f_branch2a" top: "res4f_branch2a" name: "scale4f_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4f_branch2a" top: "res4f_branch2a" name: "res4f_branch2a_relu" type: "ReLU" } layer { bottom: "res4f_branch2a" top: "res4f_branch2b" name: "res4f_branch2b" type: "Convolution" convolution_param { num_output: 256 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4f_branch2b" top: "res4f_branch2b" name: "bn4f_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4f_branch2b" top: "res4f_branch2b" name: "scale4f_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4f_branch2b" top: "res4f_branch2b" name: "res4f_branch2b_relu" type: "ReLU" } layer { bottom: "res4f_branch2b" top: "res4f_branch2c" name: "res4f_branch2c" type: "Convolution" convolution_param { num_output: 1024 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res4f_branch2c" top: "res4f_branch2c" name: "bn4f_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4f_branch2c" top: "res4f_branch2c" name: "scale4f_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4e" bottom: "res4f_branch2c" top: "res4f" name: "res4f" type: "Eltwise" } layer { bottom: "res4f" top: "res4f" name: "res4f_relu" type: "ReLU" } layer { bottom: "res4f" top: "res5a_branch1" name: "res5a_branch1" type: "Convolution" convolution_param { num_output: 2048 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res5a_branch1" top: "res5a_branch1" name: "bn5a_branch1" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5a_branch1" top: "res5a_branch1" name: "scale5a_branch1" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res4f" top: "res5a_branch2a" name: "res5a_branch2a" type: "Convolution" convolution_param { num_output: 512 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res5a_branch2a" top: "res5a_branch2a" name: "bn5a_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5a_branch2a" top: "res5a_branch2a" name: "scale5a_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5a_branch2a" top: "res5a_branch2a" name: "res5a_branch2a_relu" type: "ReLU" } layer { bottom: "res5a_branch2a" top: "res5a_branch2b" name: "res5a_branch2b" type: "Convolution" convolution_param { num_output: 512 kernel_size: 3 dilation: 2 pad: 2 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res5a_branch2b" top: "res5a_branch2b" name: "bn5a_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5a_branch2b" top: "res5a_branch2b" name: "scale5a_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5a_branch2b" top: "res5a_branch2b" name: "res5a_branch2b_relu" type: "ReLU" } layer { bottom: "res5a_branch2b" top: "res5a_branch2c" name: "res5a_branch2c" type: "Convolution" convolution_param { num_output: 2048 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res5a_branch2c" top: "res5a_branch2c" name: "bn5a_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5a_branch2c" top: "res5a_branch2c" name: "scale5a_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5a_branch1" bottom: "res5a_branch2c" top: "res5a" name: "res5a" type: "Eltwise" } layer { bottom: "res5a" top: "res5a" name: "res5a_relu" type: "ReLU" } layer { bottom: "res5a" top: "res5b_branch2a" name: "res5b_branch2a" type: "Convolution" convolution_param { num_output: 512 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res5b_branch2a" top: "res5b_branch2a" name: "bn5b_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5b_branch2a" top: "res5b_branch2a" name: "scale5b_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5b_branch2a" top: "res5b_branch2a" name: "res5b_branch2a_relu" type: "ReLU" } layer { bottom: "res5b_branch2a" top: "res5b_branch2b" name: "res5b_branch2b" type: "Convolution" convolution_param { num_output: 512 kernel_size: 3 dilation: 2 pad: 2 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res5b_branch2b" top: "res5b_branch2b" name: "bn5b_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5b_branch2b" top: "res5b_branch2b" name: "scale5b_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5b_branch2b" top: "res5b_branch2b" name: "res5b_branch2b_relu" type: "ReLU" } layer { bottom: "res5b_branch2b" top: "res5b_branch2c" name: "res5b_branch2c" type: "Convolution" convolution_param { num_output: 2048 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res5b_branch2c" top: "res5b_branch2c" name: "bn5b_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5b_branch2c" top: "res5b_branch2c" name: "scale5b_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5a" bottom: "res5b_branch2c" top: "res5b" name: "res5b" type: "Eltwise" } layer { bottom: "res5b" top: "res5b" name: "res5b_relu" type: "ReLU" } layer { bottom: "res5b" top: "res5c_branch2a" name: "res5c_branch2a" type: "Convolution" convolution_param { num_output: 512 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res5c_branch2a" top: "res5c_branch2a" name: "bn5c_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5c_branch2a" top: "res5c_branch2a" name: "scale5c_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5c_branch2a" top: "res5c_branch2a" name: "res5c_branch2a_relu" type: "ReLU" } layer { bottom: "res5c_branch2a" top: "res5c_branch2b" name: "res5c_branch2b" type: "Convolution" convolution_param { num_output: 512 kernel_size: 3 dilation: 2 pad: 2 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res5c_branch2b" top: "res5c_branch2b" name: "bn5c_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5c_branch2b" top: "res5c_branch2b" name: "scale5c_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5c_branch2b" top: "res5c_branch2b" name: "res5c_branch2b_relu" type: "ReLU" } layer { bottom: "res5c_branch2b" top: "res5c_branch2c" name: "res5c_branch2c" type: "Convolution" convolution_param { num_output: 2048 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } } layer { bottom: "res5c_branch2c" top: "res5c_branch2c" name: "bn5c_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5c_branch2c" top: "res5c_branch2c" name: "scale5c_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } } layer { bottom: "res5b" bottom: "res5c_branch2c" top: "res5c" name: "res5c" type: "Eltwise" } layer { bottom: "res5c" top: "res5c" name: "res5c_relu" type: "ReLU" } #========= RPN ============ layer { name: "rpn_conv/3x3" type: "Convolution" bottom: "res4f" top: "rpn/output" param { lr_mult: 1.0 decay_mult: 1.0 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 512 kernel_size: 3 pad: 1 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "rpn_relu/3x3" type: "ReLU" bottom: "rpn/output" top: "rpn/output" } layer { name: "rpn_cls_score" type: "Convolution" bottom: "rpn/output" top: "rpn_cls_score" param { lr_mult: 1.0 decay_mult: 1.0 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 # 2(bg/fg) * 64(anchors = num_scal * num_ratio) kernel_size: 1 pad: 0 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "rpn_bbox_pred" type: "Convolution" bottom: "rpn/output" top: "rpn_bbox_pred" param { lr_mult: 1.0 decay_mult: 1.0 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 256 # 4 * 64(anchors) kernel_size: 1 pad: 0 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { bottom: "rpn_cls_score" top: "rpn_cls_score_reshape" name: "rpn_cls_score_reshape" type: "Reshape" reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } } } #========= RoI Proposal ============ layer { name: "rpn_cls_prob" type: "Softmax" bottom: "rpn_cls_score_reshape" top: "rpn_cls_prob" } layer { name: 'rpn_cls_prob_reshape' type: 'Reshape' bottom: 'rpn_cls_prob' top: 'rpn_cls_prob_reshape' reshape_param { shape { dim: 0 dim: 128 dim: -1 dim: 0 } } # 2*64 ==> 2 * anchors } layer { name: "proposal" type: "Proposal" bottom: "rpn_cls_prob_reshape" bottom: "rpn_bbox_pred" bottom: "im_info" top: "rois" proposal_param { feat_stride: 16 base_size: 16 min_size: 16 ratio: 0.5 ratio: 1.0 ratio: 2.0 scale: 8 scale: 16 scale: 32 pre_nms_topn: 6000 post_nms_topn: 300 nms_thresh: 0.6 } } #----------------------new conv layer------------------ layer { bottom: "res5c" top: "conv_new_1" name: "conv_new_1" type: "Convolution" convolution_param { num_output: 1024 kernel_size: 1 pad: 0 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } param { lr_mult: 1.0 } param { lr_mult: 2.0 } } layer { bottom: "conv_new_1" top: "conv_new_1" name: "conv_new_1_relu" type: "ReLU" } layer { bottom: "conv_new_1" top: "rfcn_cls" name: "rfcn_cls" type: "Convolution" convolution_param { num_output: 98 #2*(7^2) cls_num*(score_maps_size^2) kernel_size: 1 pad: 0 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } param { lr_mult: 1.0 } param { lr_mult: 2.0 } } layer { bottom: "conv_new_1" top: "rfcn_bbox" name: "rfcn_bbox" type: "Convolution" convolution_param { num_output: 392 #2*4*(7^2) (bg/fg)*(dx, dy, dw, dh)*(score_maps_size^2) kernel_size: 1 pad: 0 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } param { lr_mult: 1.0 } param { lr_mult: 2.0 } } #--------------position sensitive RoI pooling-------------- layer { bottom: "rfcn_cls" bottom: "rois" top: "psroipooled_cls_rois" name: "psroipooled_cls_rois" type: "PSROIPooling" psroi_pooling_param { spatial_scale: 0.0625 output_dim: 2 #cls_num group_size: 7 } } layer { bottom: "psroipooled_cls_rois" top: "cls_score" name: "ave_cls_score_rois" type: "Pooling" pooling_param { pool: AVE kernel_size: 7 stride: 7 } } layer { bottom: "rfcn_bbox" bottom: "rois" top: "psroipooled_loc_rois" name: "psroipooled_loc_rois" type: "PSROIPooling" psroi_pooling_param { spatial_scale: 0.0625 output_dim: 8 group_size: 7 } } layer { bottom: "psroipooled_loc_rois" top: "bbox_pred_pre" name: "ave_bbox_pred_rois" type: "Pooling" pooling_param { pool: AVE kernel_size: 7 stride: 7 } } #-----------------------output------------------------ layer { name: "cls_prob" type: "Softmax" bottom: "cls_score" top: "cls_prob_pre" } # ======= Postprocessing ======== layer { name: "cls_prob_reshape" type: "Reshape" bottom: "cls_prob_pre" top: "cls_prob_reshape" reshape_param { shape { dim: 1 dim: -1 } } } layer { name: "bbox_pred_reshape" type: "Reshape" bottom: "bbox_pred_pre" top: "bbox_pred_reshape" reshape_param { shape { dim: 1 dim: -1 dim: 8 dim: 1 } } } layer { name: "bbox_pred_target_shape" type: "Reshape" bottom: "bbox_pred_pre" top: "bbox_pred_target_shape" reshape_param { shape { dim: 1 dim: -1 dim: 4 dim: 1 } } } # Split to background bounding boxes predictions and objects. layer { name: "bbox_pred_fg" type: "Crop" bottom: "bbox_pred_reshape" bottom: "bbox_pred_target_shape" top: "bbox_pred_fg" crop_param { axis: 2 offset: 4 offset: 0 } } # Reshape proposals to [1 x numPriors x 5 x 1]. layer { name: "rois_reshape" type: "Reshape" bottom: "rois" top: "rois_reshape" reshape_param { shape { dim: 1 dim: -1 dim: 5 dim: 1 } } } # Proposal layer generates [numPriors x 5] blob where 0th column are batch indices # and only the rest are bounding boxes. layer { name: "proposal_crop" type: "Crop" bottom: "rois_reshape" bottom: "bbox_pred_fg" top: "proposal_bboxes" crop_param { axis: 2 offset: 1 offset: 0 } } # Reshape it to [1 x 1 x numPriors*4 x 1] layer { name: "proposal_reshape" type: "Reshape" bottom: "proposal_bboxes" top: "proposal_reshape" reshape_param { shape { dim: 1 dim: 1 dim: -1 dim: 1 # Reshape to 4d to enable clDNN from Intel's Inference Engine } } } layer { name: "detection_out" type: "DetectionOutput" bottom: "bbox_pred_fg" bottom: "cls_prob_reshape" bottom: "proposal_reshape" top: "detection_out" detection_output_param { num_classes: 2 share_location: true background_label_id: 0 nms_param { nms_threshold: 0.3 } code_type: CENTER_SIZE keep_top_k: 100 variance_encoded_in_target: true normalized_bbox: false } } ```
ivshli commented 6 years ago

Hi, I think it's due to the anchor scale and ratio problem, but I can't find the code which define thoses pamameters, do you have any ideas or which part in the opencv code that I can look into? Thanks

dkurt commented 6 years ago

@ivshli, we cannot help you until we have no a way to reproduce an issue. Please provide reference to weights, attach a sample image or at least specify classes which this network should detect.

ivshli commented 6 years ago

Hi @dkurt Actually, I juste found the answer by changing proposal layer of rfcn_pascal_voc_resnet50.prototxt and add some convenient scales and ratios, then the model worked, hope this will help others out

Thanks for your time, I gonna close this issue.