Hello ,

I'm trying to train faster RCNN on a custom dataset with 1 classes, but for some reason I always get the following error:

F0517 00:55:59.325445 30115 smooth_L1_loss_layer.cpp:32] Check failed: bottom[0]->channels() == bottom[1]->channels() (8 vs. 84)

In which 84 is the expected output blob for the old pascal VOC dataset. But I have no idea why it's expecting the old reference instead of my (1+1)*4 output blob. The input layer has been modified to accept the 1 classes of my dataset instead of the 21 pascal VOC classes.

I'm getting the distinct feeling I'm overlooking something stupid.

Thanks in advance

For reference, heres the train.prototxt (Using vgg-16)

==================RPN================

name: "VGG_ILSVRC_16"

input: "data" input_dim: 1 input_dim: 3 input_dim: 224 input_dim: 224

input: "rois" input_dim: 1 # to be changed on-the-fly to num ROIs input_dim: 5 # [batch ind, x1, y1, x2, y2] zero-based indexing input_dim: 1 input_dim: 1

input: "labels" input_dim: 1 # to be changed on-the-fly to match num ROIs input_dim: 1 input_dim: 1 input_dim: 1

input: "bbox_targets" input_dim: 1 # to be changed on-the-fly to match num ROIs input_dim: 8 # 4 * (K+1) (=21) classes input_dim: 1 input_dim: 1

input: "bbox_loss_weights" input_dim: 1 # to be changed on-the-fly to match num ROIs input_dim: 8 # 4 * (K+1) (=21) classes input_dim: 1 input_dim: 1

layer { bottom: "data" top: "conv1_1" name: "conv1_1" type: "Convolution" param { lr_mult: 0.0 } param { lr_mult: 0.0 } convolution_param { num_output: 64 pad: 1 kernel_size: 3 } }

layer { bottom: "conv1_1" top: "conv1_1" name: "relu1_1" type: "ReLU" }

layer { bottom: "conv1_1" top: "conv1_2" name: "conv1_2" param { lr_mult: 0.0 } param { lr_mult: 0.0 } type: "Convolution" convolution_param { num_output: 64 pad: 1 kernel_size: 3 } }

layer { bottom: "conv1_2" top: "conv1_2" name: "relu1_2" type: "ReLU" }

layer { bottom: "conv1_2" top: "pool1" name: "pool1" type: "Pooling" pooling_param { pool: MAX kernel_size: 2 stride: 2 } }

layer { bottom: "pool1" top: "conv2_1" name: "conv2_1" param { lr_mult: 0.0 } param { lr_mult: 0.0 } type: "Convolution" convolution_param { num_output: 128 pad: 1 kernel_size: 3 } }

layer { bottom: "conv2_1" top: "conv2_1" name: "relu2_1" type: "ReLU" }

layer { bottom: "conv2_1" top: "conv2_2" name: "conv2_2" param { lr_mult: 0.0 } param { lr_mult: 0.0 } type: "Convolution" convolution_param { num_output: 128 pad: 1 kernel_size: 3 } }

layer { bottom: "conv2_2" top: "conv2_2" name: "relu2_2" type: "ReLU" }

layer { bottom: "conv2_2" top: "pool2" name: "pool2" type: "Pooling" pooling_param { pool: MAX kernel_size: 2 stride: 2 } }

layer { bottom: "pool2" top: "conv3_1" name: "conv3_1" param { lr_mult: 0.0 } param { lr_mult: 0.0 } type: "Convolution" convolution_param { num_output: 256 pad: 1 kernel_size: 3 } }

layer { bottom: "conv3_1" top: "conv3_1" name: "relu3_1" type: "ReLU" }

layer { bottom: "conv3_1" top: "conv3_2" name: "conv3_2" param { lr_mult: 0.0 } param { lr_mult: 0.0 } type: "Convolution" convolution_param { num_output: 256 pad: 1 kernel_size: 3 } }

layer { bottom: "conv3_2" top: "conv3_2" name: "relu3_2" type: "ReLU" }

layer { bottom: "conv3_2" top: "conv3_3" name: "conv3_3" param { lr_mult: 0.0 } param { lr_mult: 0.0 } type: "Convolution" convolution_param { num_output: 256 pad: 1 kernel_size: 3 } }

layer { bottom: "conv3_3" top: "conv3_3" name: "relu3_3" type: "ReLU" }

layer { bottom: "conv3_3" top: "pool3" name: "pool3" type: "Pooling" pooling_param { pool: MAX kernel_size: 2 stride: 2 } }

layer { bottom: "pool3" top: "conv4_1" name: "conv4_1" param { lr_mult: 0.0 } param { lr_mult: 0.0 } type: "Convolution" convolution_param { num_output: 512 pad: 1 kernel_size: 3 } }

layer { bottom: "conv4_1" top: "conv4_1" name: "relu4_1" type: "ReLU" }

layer { bottom: "conv4_1" top: "conv4_2" name: "conv4_2" param { lr_mult: 0.0 } param { lr_mult: 0.0 } type: "Convolution" convolution_param { num_output: 512 pad: 1 kernel_size: 3 } }

layer { bottom: "conv4_2" top: "conv4_2" name: "relu4_2" type: "ReLU" }

layer { bottom: "conv4_2" top: "conv4_3" name: "conv4_3" param { lr_mult: 0.0 } param { lr_mult: 0.0 } type: "Convolution" convolution_param { num_output: 512 pad: 1 kernel_size: 3 } }

layer { bottom: "conv4_3" top: "conv4_3" name: "relu4_3" type: "ReLU" }

layer { bottom: "conv4_3" top: "pool4" name: "pool4" type: "Pooling" pooling_param { pool: MAX kernel_size: 2 stride: 2 } }

layer { bottom: "pool4" top: "conv5_1" name: "conv5_1" param { lr_mult: 0.0 } param { lr_mult: 0.0 } type: "Convolution" convolution_param { num_output: 512 pad: 1 kernel_size: 3 } }

layer { bottom: "conv5_1" top: "conv5_1" name: "relu5_1" type: "ReLU" }

layer { bottom: "conv5_1" top: "conv5_2" name: "conv5_2" param { lr_mult: 0.0 } param { lr_mult: 0.0 } type: "Convolution" convolution_param { num_output: 512 pad: 1 kernel_size: 3 } }

layer { bottom: "conv5_2" top: "conv5_2" name: "relu5_2" type: "ReLU" }

layer { bottom: "conv5_2" top: "conv5_3" name: "conv5_3" param { lr_mult: 0.0 } param { lr_mult: 0.0 } type: "Convolution" convolution_param { num_output: 512 pad: 1 kernel_size: 3 } }

layer { bottom: "conv5_3" top: "conv5_3" name: "relu5_3" type: "ReLU" }

layer { bottom: "conv5_3" bottom: "rois" top: "pool5" name: "roi_pool5" type: "ROIPooling" roi_pooling_param { pooled_w: 7 pooled_h: 7 spatial_scale: 0.0625 # (1/16) } }

layer { bottom: "pool5" top: "fc6" name: "fc6" param { lr_mult: 1.0 } param { lr_mult: 2.0 } type: "InnerProduct" inner_product_param { num_output: 4096 } }

layer { bottom: "fc6" top: "fc6" name: "relu6" type: "ReLU" }

layer { bottom: "fc6" top: "fc6" name: "drop6" type: "Dropout" dropout_param { dropout_ratio: 0.5 } }

layer { bottom: "fc6" top: "fc7" name: "fc7" param { lr_mult: 1.0 } param { lr_mult: 2.0 } type: "InnerProduct" inner_product_param { num_output: 4096 } }

layer { bottom: "fc7" top: "fc7" name: "relu7" type: "ReLU" }

layer { bottom: "fc7" top: "fc7" name: "drop7" type: "Dropout" dropout_param { dropout_ratio: 0.5 } }

layer { bottom: "fc7" top: "cls_score" name: "cls_score" param { lr_mult: 1.0 } param { lr_mult: 2.0 } type: "InnerProduct" inner_product_param { num_output: 2 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }

layer { bottom: "fc7" top: "bbox_pred" name: "bbox_pred" type: "InnerProduct" param { lr_mult: 1.0 } param { lr_mult: 2.0 } inner_product_param { num_output: 8 weight_filler { type: "gaussian" std: 0.001 } bias_filler { type: "constant" value: 0 } } }

layer { name: "loss" type: "SoftmaxWithLoss" bottom: "cls_score" bottom: "labels" top: "loss_cls" loss_weight: 1 }

layer { name: "accuarcy" type: "Accuracy" bottom: "cls_score" bottom: "labels" top: "accuarcy" }

layer { name: "loss_bbox" type: "SmoothL1Loss" bottom: "bbox_pred" bottom: "bbox_targets" bottom: "bbox_loss_weights" top: "loss_bbox" loss_weight: 1 }

=========fast RCNN ============