# Conversion from caffemodel to TensorRT

**URL:** <https://forums.developer.nvidia.com/t/conversion-from-caffemodel-to-tensorrt/115622>\
**Category:** Jetson Nano\
**Tags:** tensorrt\
**Created:** [March 16, 2020, 1:49pm UTC](https://forums.developer.nvidia.com/t/conversion-from-caffemodel-to-tensorrt/115622 "2020-03-16T13:49:28Z")\
**Posts on this page:** 8\
**Page:** 1

<div class="post-metadata">

**Author:** ![andrea.ganassa](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@andrea.ganassa](https://forums.developer.nvidia.com/u/andrea.ganassa)\
**Post date:** [March 16, 2020, 1:49pm UTC](https://forums.developer.nvidia.com/t/conversion-from-caffemodel-to-tensorrt/115622/1 "2020-03-16T13:49:28Z")

</div>

Hello everyone,  
i am trying to convert some caffemodels to tensorRt, my first question is if a model contains all the supported operations is it able to convert?  
In particular I am trying to convert this model:  
res10\_300x300\_ssd\_iter\_140000\_fp16.caffemodel, but it returns me this error "log (): mbox\_loc: all concat input tensors must have the same dimensions except on the concatenation axis (0), but dimensions mismatched at input 1 at index 1. Input 0 shape: [38 , 38,16], Input 1 shape: [19,19,24] "is there anyone who has any idea about it?

---

<div class="post-metadata">

**Author:** ![AastaLLL](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/aastalll/32/14043_2.png) [@AastaLLL](https://forums.developer.nvidia.com/u/AastaLLL)\
**Post date:** [March 17, 2020, 6:10am UTC](https://forums.developer.nvidia.com/t/conversion-from-caffemodel-to-tensorrt/115622/3 "2020-03-17T06:10:09Z")

</div>

Hi,

Could you try your model with _trtexec_ and share the corresponding log with us?

`/usr/src/tensorrt/bin/trtexec --deploy=[your_model.prototxt] --output=[output_layer_name] --verbose`

Thanks.

---

<div class="post-metadata">

**Author:** ![andrea.ganassa](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@andrea.ganassa](https://forums.developer.nvidia.com/u/andrea.ganassa)\
**Post date:** [March 17, 2020, 9:26am UTC](https://forums.developer.nvidia.com/t/conversion-from-caffemodel-to-tensorrt/115622/5 "2020-03-17T09:26:16Z")

</div>

&&&& RUNNING TensorRT.trtexec # /usr/src/tensorrt/bin/trtexec --deploy=/home/dlinano/deepstream\_sdk\_v4.0.2\_jetson/sources/apps/redaction\_with\_deepstream/fd\_lpd\_model/face\_deploy.prototxt --output=detection\_out --verbose  
[02/17/2020-02:19:01] [I] === Model Options ===  
[02/17/2020-02:19:01] [I] Format: Caffe  
[02/17/2020-02:19:01] [I] Model:  
[02/17/2020-02:19:01] [I] Prototxt: /home/dlinano/deepstream\_sdk\_v4.0.2\_jetson/sources/apps/redaction\_with\_deepstream/fd\_lpd\_model/face\_deploy.prototxtOutput: detection\_out  
[02/17/2020-02:19:01] [I] === Build Options ===  
[02/17/2020-02:19:01] [I] Max batch: 1  
[02/17/2020-02:19:01] [I] Workspace: 16 MB  
[02/17/2020-02:19:01] [I] minTiming: 1  
[02/17/2020-02:19:01] [I] avgTiming: 8  
[02/17/2020-02:19:01] [I] Precision: FP32  
[02/17/2020-02:19:01] [I] Calibration:  
[02/17/2020-02:19:01] [I] Safe mode: Disabled  
[02/17/2020-02:19:01] [I] Save engine:  
[02/17/2020-02:19:01] [I] Load engine:  
[02/17/2020-02:19:01] [I] Inputs format: fp32:CHW  
[02/17/2020-02:19:01] [I] Outputs format: fp32:CHW  
[02/17/2020-02:19:01] [I] Input build shapes: model  
[02/17/2020-02:19:01] [I] === System Options ===  
[02/17/2020-02:19:01] [I] Device: 0  
[02/17/2020-02:19:01] [I] DLACore:  
[02/17/2020-02:19:01] [I] Plugins:  
[02/17/2020-02:19:01] [I] === Inference Options ===  
[02/17/2020-02:19:01] [I] Batch: 1  
[02/17/2020-02:19:01] [I] Iterations: 10 (200 ms warm up)  
[02/17/2020-02:19:01] [I] Duration: 10s  
[02/17/2020-02:19:01] [I] Sleep time: 0ms  
[02/17/2020-02:19:01] [I] Streams: 1  
[02/17/2020-02:19:01] [I] Spin-wait: Disabled  
[02/17/2020-02:19:01] [I] Multithreading: Enabled  
[02/17/2020-02:19:01] [I] CUDA Graph: Disabled  
[02/17/2020-02:19:01] [I] Skip inference: Disabled  
[02/17/2020-02:19:01] [I] Input inference shapes: model  
[02/17/2020-02:19:01] [I] === Reporting Options ===  
[02/17/2020-02:19:01] [I] Verbose: Enabled  
[02/17/2020-02:19:01] [I] Averages: 10 inferences  
[02/17/2020-02:19:01] [I] Percentile: 99  
[02/17/2020-02:19:01] [I] Dump output: Disabled  
[02/17/2020-02:19:01] [I] Profile: Disabled  
[02/17/2020-02:19:01] [I] Export timing to JSON file:  
[02/17/2020-02:19:01] [I] Export profile to JSON file:  
[02/17/2020-02:19:01] [I]  
[02/17/2020-02:19:01] [V] [TRT] Plugin Creator registration succeeded - GridAnchor\_TRT  
[02/17/2020-02:19:01] [V] [TRT] Plugin Creator registration succeeded - GridAnchorRect\_TRT  
[02/17/2020-02:19:01] [V] [TRT] Plugin Creator registration succeeded - NMS\_TRT  
[02/17/2020-02:19:01] [V] [TRT] Plugin Creator registration succeeded - Reorg\_TRT  
[02/17/2020-02:19:01] [V] [TRT] Plugin Creator registration succeeded - Region\_TRT  
[02/17/2020-02:19:01] [V] [TRT] Plugin Creator registration succeeded - Clip\_TRT  
[02/17/2020-02:19:01] [V] [TRT] Plugin Creator registration succeeded - LReLU\_TRT  
[02/17/2020-02:19:01] [V] [TRT] Plugin Creator registration succeeded - PriorBox\_TRT  
[02/17/2020-02:19:01] [V] [TRT] Plugin Creator registration succeeded - Normalize\_TRT  
[02/17/2020-02:19:01] [V] [TRT] Plugin Creator registration succeeded - RPROI\_TRT  
[02/17/2020-02:19:01] [V] [TRT] Plugin Creator registration succeeded - BatchedNMS\_TRT  
[02/17/2020-02:19:01] [V] [TRT] Plugin Creator registration succeeded - FlattenConcat\_TRT  
Warning: Flatten layer ignored. TensorRT implicitly flattens input to FullyConnected layers, but in other circumstances this will result in undefined behavior.  
Warning: Flatten layer ignored. TensorRT implicitly flattens input to FullyConnected layers, but in other circumstances this will result in undefined behavior.  
Warning: Flatten layer ignored. TensorRT implicitly flattens input to FullyConnected layers, but in other circumstances this will result in undefined behavior.  
Warning: Flatten layer ignored. TensorRT implicitly flattens input to FullyConnected layers, but in other circumstances this will result in undefined behavior.  
Warning: Flatten layer ignored. TensorRT implicitly flattens input to FullyConnected layers, but in other circumstances this will result in undefined behavior.  
Warning: Flatten layer ignored. TensorRT implicitly flattens input to FullyConnected layers, but in other circumstances this will result in undefined behavior.  
Warning: Flatten layer ignored. TensorRT implicitly flattens input to FullyConnected layers, but in other circumstances this will result in undefined behavior.  
Warning: Flatten layer ignored. TensorRT implicitly flattens input to FullyConnected layers, but in other circumstances this will result in undefined behavior.  
Warning: Flatten layer ignored. TensorRT implicitly flattens input to FullyConnected layers, but in other circumstances this will result in undefined behavior.  
Warning: Flatten layer ignored. TensorRT implicitly flattens input to FullyConnected layers, but in other circumstances this will result in undefined behavior.  
Warning: Flatten layer ignored. TensorRT implicitly flattens input to FullyConnected layers, but in other circumstances this will result in undefined behavior.  
Warning: Flatten layer ignored. TensorRT implicitly flattens input to FullyConnected layers, but in other circumstances this will result in undefined behavior.  
[02/17/2020-02:19:02] [E] [TRT] mbox\_loc: all concat input tensors must have the same dimensions except on the concatenation axis (0), but dimensions mismatched at input 1 at index 1. Input 0 shape: [38,38,16], Input 1 shape: [19,19,24]  
[02/17/2020-02:19:02] [E] [TRT] mbox\_conf: all concat input tensors must have the same dimensions except on the concatenation axis (0), but dimensions mismatched at input 1 at index 1. Input 0 shape: [38,38,8], Input 1 shape: [19,19,12]  
Caffe Parser: Invalid axis in softmax layer - TensorRT expects NCHW input. Negative axis is not supported in TensorRT, please use positive axis indexing  
error parsing layer type Softmax index 109  
[02/17/2020-02:19:02] [E] Failed to parse caffe model or prototxt, tensors blob not found  
[02/17/2020-02:19:02] [E] Parsing model failed  
[02/17/2020-02:19:02] [E] Engine could not be created  
&&&& FAILED TensorRT.trtexec # /usr/src/tensorrt/bin/trtexec --deploy=/home/dlinano/deepstream\_sdk\_v4.0.2\_jetson/sources/apps/redaction\_with\_deepstream/fd\_lpd\_model/face\_deploy.prototxt --output=detection\_out --verbose

---

<div class="post-metadata">

**Author:** ![AastaLLL](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/aastalll/32/14043_2.png) [@AastaLLL](https://forums.developer.nvidia.com/u/AastaLLL)\
**Post date:** [March 27, 2020, 8:20am UTC](https://forums.developer.nvidia.com/t/conversion-from-caffemodel-to-tensorrt/115622/6 "2020-03-27T08:20:55Z")

</div>

Hi,

Sorry for the late update.

Our Caffe parser doesn’t support _Flatten_ layer.  
However, you can use _Reshape_ layer to do the identical transform instead.

```auto
reshape_param {
  shape {
    dim: 0
    dim: -1
    dim: 1
    dim: 1
  }
}
```

There is another issue in your model, please add one more output in the _detection\_out_ layer to match the _DetectionOutput_ plugin requirement.

```auto
top: "keep_count"
```

A similar change can also be found in our SSD sample:

```auto
/usr/src/tensorrt/samples/sampleSSD/
```

Expecting you are using the model shared in the below GitHub, we also make an workable deploy.prototxt for you:  
https://github.com/spmallick/learnopencv/tree/master/FaceDetectionComparison/models

```auto
input: "data"
input_shape {
  dim: 1
  dim: 3
  dim: 300
  dim: 300
}

layer {
  name: "data_bn"
  type: "BatchNorm"
  bottom: "data"
  top: "data_bn"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "data_scale"
  type: "Scale"
  bottom: "data_bn"
  top: "data_bn"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv1_h"
  type: "Convolution"
  bottom: "data_bn"
  top: "conv1_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    pad: 3
    kernel_size: 7
    stride: 2
    weight_filler {
      type: "msra"
      variance_norm: FAN_OUT
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "conv1_bn_h"
  type: "BatchNorm"
  bottom: "conv1_h"
  top: "conv1_h"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "conv1_scale_h"
  type: "Scale"
  bottom: "conv1_h"
  top: "conv1_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv1_relu"
  type: "ReLU"
  bottom: "conv1_h"
  top: "conv1_h"
}
layer {
  name: "conv1_pool"
  type: "Pooling"
  bottom: "conv1_h"
  top: "conv1_pool"
  pooling_param {
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "layer_64_1_conv1_h"
  type: "Convolution"
  bottom: "conv1_pool"
  top: "layer_64_1_conv1_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_64_1_bn2_h"
  type: "BatchNorm"
  bottom: "layer_64_1_conv1_h"
  top: "layer_64_1_conv1_h"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "layer_64_1_scale2_h"
  type: "Scale"
  bottom: "layer_64_1_conv1_h"
  top: "layer_64_1_conv1_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_64_1_relu2"
  type: "ReLU"
  bottom: "layer_64_1_conv1_h"
  top: "layer_64_1_conv1_h"
}
layer {
  name: "layer_64_1_conv2_h"
  type: "Convolution"
  bottom: "layer_64_1_conv1_h"
  top: "layer_64_1_conv2_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_64_1_sum"
  type: "Eltwise"
  bottom: "layer_64_1_conv2_h"
  bottom: "conv1_pool"
  top: "layer_64_1_sum"
}
layer {
  name: "layer_128_1_bn1_h"
  type: "BatchNorm"
  bottom: "layer_64_1_sum"
  top: "layer_128_1_bn1_h"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "layer_128_1_scale1_h"
  type: "Scale"
  bottom: "layer_128_1_bn1_h"
  top: "layer_128_1_bn1_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_128_1_relu1"
  type: "ReLU"
  bottom: "layer_128_1_bn1_h"
  top: "layer_128_1_bn1_h"
}
layer {
  name: "layer_128_1_conv1_h"
  type: "Convolution"
  bottom: "layer_128_1_bn1_h"
  top: "layer_128_1_conv1_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_1_bn2"
  type: "BatchNorm"
  bottom: "layer_128_1_conv1_h"
  top: "layer_128_1_conv1_h"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "layer_128_1_scale2"
  type: "Scale"
  bottom: "layer_128_1_conv1_h"
  top: "layer_128_1_conv1_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_128_1_relu2"
  type: "ReLU"
  bottom: "layer_128_1_conv1_h"
  top: "layer_128_1_conv1_h"
}
layer {
  name: "layer_128_1_conv2"
  type: "Convolution"
  bottom: "layer_128_1_conv1_h"
  top: "layer_128_1_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_1_conv_expand_h"
  type: "Convolution"
  bottom: "layer_128_1_bn1_h"
  top: "layer_128_1_conv_expand_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_1_sum"
  type: "Eltwise"
  bottom: "layer_128_1_conv2"
  bottom: "layer_128_1_conv_expand_h"
  top: "layer_128_1_sum"
}
layer {
  name: "layer_256_1_bn1"
  type: "BatchNorm"
  bottom: "layer_128_1_sum"
  top: "layer_256_1_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "layer_256_1_scale1"
  type: "Scale"
  bottom: "layer_256_1_bn1"
  top: "layer_256_1_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_1_relu1"
  type: "ReLU"
  bottom: "layer_256_1_bn1"
  top: "layer_256_1_bn1"
}
layer {
  name: "layer_256_1_conv1"
  type: "Convolution"
  bottom: "layer_256_1_bn1"
  top: "layer_256_1_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_1_bn2"
  type: "BatchNorm"
  bottom: "layer_256_1_conv1"
  top: "layer_256_1_conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "layer_256_1_scale2"
  type: "Scale"
  bottom: "layer_256_1_conv1"
  top: "layer_256_1_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_1_relu2"
  type: "ReLU"
  bottom: "layer_256_1_conv1"
  top: "layer_256_1_conv1"
}
layer {
  name: "layer_256_1_conv2"
  type: "Convolution"
  bottom: "layer_256_1_conv1"
  top: "layer_256_1_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_1_conv_expand"
  type: "Convolution"
  bottom: "layer_256_1_bn1"
  top: "layer_256_1_conv_expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_1_sum"
  type: "Eltwise"
  bottom: "layer_256_1_conv2"
  bottom: "layer_256_1_conv_expand"
  top: "layer_256_1_sum"
}
layer {
  name: "layer_512_1_bn1"
  type: "BatchNorm"
  bottom: "layer_256_1_sum"
  top: "layer_512_1_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "layer_512_1_scale1"
  type: "Scale"
  bottom: "layer_512_1_bn1"
  top: "layer_512_1_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_512_1_relu1"
  type: "ReLU"
  bottom: "layer_512_1_bn1"
  top: "layer_512_1_bn1"
}
layer {
  name: "layer_512_1_conv1_h"
  type: "Convolution"
  bottom: "layer_512_1_bn1"
  top: "layer_512_1_conv1_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1 # 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_512_1_bn2_h"
  type: "BatchNorm"
  bottom: "layer_512_1_conv1_h"
  top: "layer_512_1_conv1_h"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "layer_512_1_scale2_h"
  type: "Scale"
  bottom: "layer_512_1_conv1_h"
  top: "layer_512_1_conv1_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_512_1_relu2"
  type: "ReLU"
  bottom: "layer_512_1_conv1_h"
  top: "layer_512_1_conv1_h"
}
layer {
  name: "layer_512_1_conv2_h"
  type: "Convolution"
  bottom: "layer_512_1_conv1_h"
  top: "layer_512_1_conv2_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 2 # 1
    kernel_size: 3
    stride: 1
    dilation: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_512_1_conv_expand_h"
  type: "Convolution"
  bottom: "layer_512_1_bn1"
  top: "layer_512_1_conv_expand_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1 # 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_512_1_sum"
  type: "Eltwise"
  bottom: "layer_512_1_conv2_h"
  bottom: "layer_512_1_conv_expand_h"
  top: "layer_512_1_sum"
}
layer {
  name: "last_bn_h"
  type: "BatchNorm"
  bottom: "layer_512_1_sum"
  top: "layer_512_1_sum"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "last_scale_h"
  type: "Scale"
  bottom: "layer_512_1_sum"
  top: "layer_512_1_sum"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "last_relu"
  type: "ReLU"
  bottom: "layer_512_1_sum"
  top: "fc7"
}

layer {
  name: "conv6_1_h"
  type: "Convolution"
  bottom: "fc7"
  top: "conv6_1_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv6_1_relu"
  type: "ReLU"
  bottom: "conv6_1_h"
  top: "conv6_1_h"
}
layer {
  name: "conv6_2_h"
  type: "Convolution"
  bottom: "conv6_1_h"
  top: "conv6_2_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv6_2_relu"
  type: "ReLU"
  bottom: "conv6_2_h"
  top: "conv6_2_h"
}
layer {
  name: "conv7_1_h"
  type: "Convolution"
  bottom: "conv6_2_h"
  top: "conv7_1_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv7_1_relu"
  type: "ReLU"
  bottom: "conv7_1_h"
  top: "conv7_1_h"
}
layer {
  name: "conv7_2_h"
  type: "Convolution"
  bottom: "conv7_1_h"
  top: "conv7_2_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv7_2_relu"
  type: "ReLU"
  bottom: "conv7_2_h"
  top: "conv7_2_h"
}
layer {
  name: "conv8_1_h"
  type: "Convolution"
  bottom: "conv7_2_h"
  top: "conv8_1_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv8_1_relu"
  type: "ReLU"
  bottom: "conv8_1_h"
  top: "conv8_1_h"
}
layer {
  name: "conv8_2_h"
  type: "Convolution"
  bottom: "conv8_1_h"
  top: "conv8_2_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv8_2_relu"
  type: "ReLU"
  bottom: "conv8_2_h"
  top: "conv8_2_h"
}
layer {
  name: "conv9_1_h"
  type: "Convolution"
  bottom: "conv8_2_h"
  top: "conv9_1_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv9_1_relu"
  type: "ReLU"
  bottom: "conv9_1_h"
  top: "conv9_1_h"
}
layer {
  name: "conv9_2_h"
  type: "Convolution"
  bottom: "conv9_1_h"
  top: "conv9_2_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv9_2_relu"
  type: "ReLU"
  bottom: "conv9_2_h"
  top: "conv9_2_h"
}
layer {
  name: "conv4_3_norm"
  type: "Normalize"
  bottom: "layer_256_1_bn1"
  top: "conv4_3_norm"
  norm_param {
    across_spatial: false
    scale_filler {
      type: "constant"
      value: 20
    }
    channel_shared: false
  }
}
layer {
  name: "conv4_3_norm_mbox_loc"
  type: "Convolution"
  bottom: "conv4_3_norm"
  top: "conv4_3_norm_mbox_loc"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv4_3_norm_mbox_loc_perm"
  type: "Permute"
  bottom: "conv4_3_norm_mbox_loc"
  top: "conv4_3_norm_mbox_loc_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv4_3_norm_mbox_loc_flat"
  type: "Reshape"
  bottom: "conv4_3_norm_mbox_loc_perm"
  top: "conv4_3_norm_mbox_loc_flat"
  reshape_param {
    shape {
      dim: 0
      dim: -1
      dim: 1
      dim: 1
    }
  }
}
layer {
  name: "conv4_3_norm_mbox_conf"
  type: "Convolution"
  bottom: "conv4_3_norm"
  top: "conv4_3_norm_mbox_conf"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 8 # 84
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv4_3_norm_mbox_conf_perm"
  type: "Permute"
  bottom: "conv4_3_norm_mbox_conf"
  top: "conv4_3_norm_mbox_conf_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv4_3_norm_mbox_conf_flat"
  type: "Reshape"
  bottom: "conv4_3_norm_mbox_conf_perm"
  top: "conv4_3_norm_mbox_conf_flat"
  reshape_param {
    shape {
      dim: 0
      dim: -1
      dim: 1
      dim: 1
    }
  }
}
layer {
  name: "conv4_3_norm_mbox_priorbox"
  type: "PriorBox"
  bottom: "conv4_3_norm"
  bottom: "data"
  top: "conv4_3_norm_mbox_priorbox"
  prior_box_param {
    min_size: 30.0
    max_size: 60.0
    aspect_ratio: 2
    flip: true
    clip: false
    variance: 0.1
    variance: 0.1
    variance: 0.2
    variance: 0.2
    step: 8
    offset: 0.5
  }
}
layer {
  name: "fc7_mbox_loc"
  type: "Convolution"
  bottom: "fc7"
  top: "fc7_mbox_loc"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 24
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "fc7_mbox_loc_perm"
  type: "Permute"
  bottom: "fc7_mbox_loc"
  top: "fc7_mbox_loc_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "fc7_mbox_loc_flat"
  type: "Reshape"
  bottom: "fc7_mbox_loc_perm"
  top: "fc7_mbox_loc_flat"
  reshape_param {
    shape {
      dim: 0
      dim: -1
      dim: 1
      dim: 1
    }
  }
}
layer {
  name: "fc7_mbox_conf"
  type: "Convolution"
  bottom: "fc7"
  top: "fc7_mbox_conf"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 12 # 126
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "fc7_mbox_conf_perm"
  type: "Permute"
  bottom: "fc7_mbox_conf"
  top: "fc7_mbox_conf_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "fc7_mbox_conf_flat"
  type: "Reshape"
  bottom: "fc7_mbox_conf_perm"
  top: "fc7_mbox_conf_flat"
  reshape_param {
    shape {
      dim: 0
      dim: -1
      dim: 1
      dim: 1
    }
  }
}
layer {
  name: "fc7_mbox_priorbox"
  type: "PriorBox"
  bottom: "fc7"
  bottom: "data"
  top: "fc7_mbox_priorbox"
  prior_box_param {
    min_size: 60.0
    max_size: 111.0
    aspect_ratio: 2
    aspect_ratio: 3
    flip: true
    clip: false
    variance: 0.1
    variance: 0.1
    variance: 0.2
    variance: 0.2
    step: 16
    offset: 0.5
  }
}
layer {
  name: "conv6_2_mbox_loc"
  type: "Convolution"
  bottom: "conv6_2_h"
  top: "conv6_2_mbox_loc"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 24
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv6_2_mbox_loc_perm"
  type: "Permute"
  bottom: "conv6_2_mbox_loc"
  top: "conv6_2_mbox_loc_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv6_2_mbox_loc_flat"
  type: "Reshape"
  bottom: "conv6_2_mbox_loc_perm"
  top: "conv6_2_mbox_loc_flat"
  reshape_param {
    shape {
      dim: 0
      dim: -1
      dim: 1
      dim: 1
    }
  }
}
layer {
  name: "conv6_2_mbox_conf"
  type: "Convolution"
  bottom: "conv6_2_h"
  top: "conv6_2_mbox_conf"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 12 # 126
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv6_2_mbox_conf_perm"
  type: "Permute"
  bottom: "conv6_2_mbox_conf"
  top: "conv6_2_mbox_conf_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv6_2_mbox_conf_flat"
  type: "Reshape"
  bottom: "conv6_2_mbox_conf_perm"
  top: "conv6_2_mbox_conf_flat"
  reshape_param {
    shape {
      dim: 0
      dim: -1
      dim: 1
      dim: 1
    }
  }
}
layer {
  name: "conv6_2_mbox_priorbox"
  type: "PriorBox"
  bottom: "conv6_2_h"
  bottom: "data"
  top: "conv6_2_mbox_priorbox"
  prior_box_param {
    min_size: 111.0
    max_size: 162.0
    aspect_ratio: 2
    aspect_ratio: 3
    flip: true
    clip: false
    variance: 0.1
    variance: 0.1
    variance: 0.2
    variance: 0.2
    step: 32
    offset: 0.5
  }
}
layer {
  name: "conv7_2_mbox_loc"
  type: "Convolution"
  bottom: "conv7_2_h"
  top: "conv7_2_mbox_loc"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 24
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv7_2_mbox_loc_perm"
  type: "Permute"
  bottom: "conv7_2_mbox_loc"
  top: "conv7_2_mbox_loc_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv7_2_mbox_loc_flat"
  type: "Reshape"
  bottom: "conv7_2_mbox_loc_perm"
  top: "conv7_2_mbox_loc_flat"
  reshape_param {
    shape {
      dim: 0
      dim: -1
      dim: 1
      dim: 1
    }
  }
}
layer {
  name: "conv7_2_mbox_conf"
  type: "Convolution"
  bottom: "conv7_2_h"
  top: "conv7_2_mbox_conf"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 12 # 126
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv7_2_mbox_conf_perm"
  type: "Permute"
  bottom: "conv7_2_mbox_conf"
  top: "conv7_2_mbox_conf_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv7_2_mbox_conf_flat"
  type: "Reshape"
  bottom: "conv7_2_mbox_conf_perm"
  top: "conv7_2_mbox_conf_flat"
  reshape_param {
    shape {
      dim: 0
      dim: -1
      dim: 1
      dim: 1
    }
  }
}
layer {
  name: "conv7_2_mbox_priorbox"
  type: "PriorBox"
  bottom: "conv7_2_h"
  bottom: "data"
  top: "conv7_2_mbox_priorbox"
  prior_box_param {
    min_size: 162.0
    max_size: 213.0
    aspect_ratio: 2
    aspect_ratio: 3
    flip: true
    clip: false
    variance: 0.1
    variance: 0.1
    variance: 0.2
    variance: 0.2
    step: 64
    offset: 0.5
  }
}
layer {
  name: "conv8_2_mbox_loc"
  type: "Convolution"
  bottom: "conv8_2_h"
  top: "conv8_2_mbox_loc"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv8_2_mbox_loc_perm"
  type: "Permute"
  bottom: "conv8_2_mbox_loc"
  top: "conv8_2_mbox_loc_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv8_2_mbox_loc_flat"
  type: "Reshape"
  bottom: "conv8_2_mbox_loc_perm"
  top: "conv8_2_mbox_loc_flat"
  reshape_param {
    shape {
      dim: 0
      dim: -1
      dim: 1
      dim: 1
    }
  }
}
layer {
  name: "conv8_2_mbox_conf"
  type: "Convolution"
  bottom: "conv8_2_h"
  top: "conv8_2_mbox_conf"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 8 # 84
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv8_2_mbox_conf_perm"
  type: "Permute"
  bottom: "conv8_2_mbox_conf"
  top: "conv8_2_mbox_conf_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv8_2_mbox_conf_flat"
  type: "Reshape"
  bottom: "conv8_2_mbox_conf_perm"
  top: "conv8_2_mbox_conf_flat"
  reshape_param {
    shape {
      dim: 0
      dim: -1
      dim: 1
      dim: 1
    }
  }
}
layer {
  name: "conv8_2_mbox_priorbox"
  type: "PriorBox"
  bottom: "conv8_2_h"
  bottom: "data"
  top: "conv8_2_mbox_priorbox"
  prior_box_param {
    min_size: 213.0
    max_size: 264.0
    aspect_ratio: 2
    flip: true
    clip: false
    variance: 0.1
    variance: 0.1
    variance: 0.2
    variance: 0.2
    step: 100
    offset: 0.5
  }
}
layer {
  name: "conv9_2_mbox_loc"
  type: "Convolution"
  bottom: "conv9_2_h"
  top: "conv9_2_mbox_loc"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv9_2_mbox_loc_perm"
  type: "Permute"
  bottom: "conv9_2_mbox_loc"
  top: "conv9_2_mbox_loc_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv9_2_mbox_loc_flat"
  type: "Reshape"
  bottom: "conv9_2_mbox_loc_perm"
  top: "conv9_2_mbox_loc_flat"
  reshape_param {
    shape {
      dim: 0
      dim: -1
      dim: 1
      dim: 1
    }
  }
}
layer {
  name: "conv9_2_mbox_conf"
  type: "Convolution"
  bottom: "conv9_2_h"
  top: "conv9_2_mbox_conf"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 8 # 84
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv9_2_mbox_conf_perm"
  type: "Permute"
  bottom: "conv9_2_mbox_conf"
  top: "conv9_2_mbox_conf_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv9_2_mbox_conf_flat"
  type: "Reshape"
  bottom: "conv9_2_mbox_conf_perm"
  top: "conv9_2_mbox_conf_flat"
  reshape_param {
    shape {
      dim: 0
      dim: -1
      dim: 1
      dim: 1
    }
  }
}
layer {
  name: "conv9_2_mbox_priorbox"
  type: "PriorBox"
  bottom: "conv9_2_h"
  bottom: "data"
  top: "conv9_2_mbox_priorbox"
  prior_box_param {
    min_size: 264.0
    max_size: 315.0
    aspect_ratio: 2
    flip: true
    clip: false
    variance: 0.1
    variance: 0.1
    variance: 0.2
    variance: 0.2
    step: 300
    offset: 0.5
  }
}
layer {
  name: "mbox_loc"
  type: "Concat"
  bottom: "conv4_3_norm_mbox_loc_flat"
  bottom: "fc7_mbox_loc_flat"
  bottom: "conv6_2_mbox_loc_flat"
  bottom: "conv7_2_mbox_loc_flat"
  bottom: "conv8_2_mbox_loc_flat"
  bottom: "conv9_2_mbox_loc_flat"
  top: "mbox_loc"
  concat_param {
    axis: 1
  }
}
layer {
  name: "mbox_conf"
  type: "Concat"
  bottom: "conv4_3_norm_mbox_conf_flat"
  bottom: "fc7_mbox_conf_flat"
  bottom: "conv6_2_mbox_conf_flat"
  bottom: "conv7_2_mbox_conf_flat"
  bottom: "conv8_2_mbox_conf_flat"
  bottom: "conv9_2_mbox_conf_flat"
  top: "mbox_conf"
  concat_param {
    axis: 1
  }
}
layer {
  name: "mbox_priorbox"
  type: "Concat"
  bottom: "conv4_3_norm_mbox_priorbox"
  bottom: "fc7_mbox_priorbox"
  bottom: "conv6_2_mbox_priorbox"
  bottom: "conv7_2_mbox_priorbox"
  bottom: "conv8_2_mbox_priorbox"
  bottom: "conv9_2_mbox_priorbox"
  top: "mbox_priorbox"
  concat_param {
    axis: 2
  }
}

layer {
  name: "mbox_conf_reshape"
  type: "Reshape"
  bottom: "mbox_conf"
  top: "mbox_conf_reshape"
  reshape_param {
    shape {
      dim: 0
      dim: -1
      dim: 2
    }
  }
}
layer {
  name: "mbox_conf_softmax"
  type: "Softmax"
  bottom: "mbox_conf_reshape"
  top: "mbox_conf_softmax"
  softmax_param {
    axis: 2
  }
}
layer {
  name: "mbox_conf_flatten"
  type: "Reshape"
  bottom: "mbox_conf_softmax"
  top: "mbox_conf_flatten"
  reshape_param {
    shape {
      dim: 0
      dim: -1
      dim: 1
      dim: 1
    }
  }
}

layer {
  name: "detection_out"
  type: "DetectionOutput"
  bottom: "mbox_loc"
  bottom: "mbox_conf_flatten"
  bottom: "mbox_priorbox"
  top: "detection_out"
  top: "keep_count"
  include {
    phase: TEST
  }
  detection_output_param {
    num_classes: 2
    share_location: true
    background_label_id: 0
    nms_param {
      nms_threshold: 0.3
      top_k: 400
    }
    code_type: CENTER_SIZE
    keep_top_k: 200
    confidence_threshold: 0.01
  }
}
```

Thanks.

---

<div class="post-metadata">

**Author:** ![andrea.ganassa](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@andrea.ganassa](https://forums.developer.nvidia.com/u/andrea.ganassa)\
**Post date:** [March 27, 2020, 5:04pm UTC](https://forums.developer.nvidia.com/t/conversion-from-caffemodel-to-tensorrt/115622/7 "2020-03-27T17:04:11Z")

</div>

Thanks for the reply,

I used your deploy.prototxt and I correctly converted in TensorRT, but unfortunately with a simple detector program like your “DeepStream 4.2.1 Exercise” the output:

0:05:22.716828271 25799 0x55895d0000 ERROR nvinfer gstnvinfer.cpp:511:gst\_nvinfer\_logger: NvDsInferContext[UID 1]:parseBoundingBox(): Could not find output coverage layer for parsing objects  
0:05:22.716895668 25799 0x55895d0000 ERROR nvinfer gstnvinfer.cpp:511:gst\_nvinfer\_logger: NvDsInferContext[UID 1]:fillDetectionOutput(): Failed to parse bboxes  
Segmentation fault (core dumped)

how can I solve this?

Best Regards,  
Andrea

---

<div class="post-metadata">

**Author:** ![AastaLLL](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/aastalll/32/14043_2.png) [@AastaLLL](https://forums.developer.nvidia.com/u/AastaLLL)\
**Post date:** [April 8, 2020, 2:45am UTC](https://forums.developer.nvidia.com/t/conversion-from-caffemodel-to-tensorrt/115622/8 "2020-04-08T02:45:40Z")

</div>

Hi,

The output layer name change in the this model.  
Please update it correctly.

Thanks.

---

<div class="post-metadata">

**Author:** ![god\_ra](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@god\_ra](https://forums.developer.nvidia.com/u/god_ra)\
**Post date:** [June 18, 2020, 6:49pm UTC](https://forums.developer.nvidia.com/t/conversion-from-caffemodel-to-tensorrt/115622/10 "2020-06-18T18:49:52Z")

</div>

Hi,

Did you train your model using resnet in caffe?? or VGG 16 in caffe??

I wanted to train my model in caffe to run inference. But I want to know which repo is best for TRAINING OWN DATASET.

---

<div class="post-metadata">

**Author:** ![nadeemm](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/nadeemm/32/367020_2.png) [@nadeemm](https://forums.developer.nvidia.com/u/nadeemm)\
**Post date:** [October 18, 2021, 5:40pm UTC](https://forums.developer.nvidia.com/t/conversion-from-caffemodel-to-tensorrt/115622/11 "2021-10-18T17:40:21Z")

</div>

Closed on October 18, 2021, 5:40pm UTC