Train Multiple classes in DetectNet

To train a DetectNet that can recognize multiple classes, you need to modify a few lines of the DetectNet.prototxt. There are total 7 places of the code you need to modify.

 

1. train_transform layer and val_transform

   In the train_transform layer and val_transform layer(around line 76 and 116)
   add object_class fields that match your number of classes

   For example, if you have 3 classes, you need to add

   object_class: { src: 1 dst: 0}
   object_class: { src: 2 dst: 1}
   object_class: { src: 3 dst: 2}
   

2. Number of output in convolution parameter

   Change the num_output equal to your number of classes(around line 2379)

   For example, if you have 3 classes, you need to change

   num_output: 3
   

3. Cluster bboxes’ layer

   add more top layer parameter (around line 2499)

   For example, if you have 3 classes, you need to add

   top: 'bbox-list-class0'
   top: 'bbox-list-class1'
   top: 'bbox-list-class2'
   

4. param_str value in Cluster bboxes’ layer

   change the last parameter of the param_str

   param_str : ‘1920, 1152, 16, 0.05, 1, 0.02, 22, NUM_OF_CLASSES’

   param_str : '1920, 1152, 16, 0.05, 1, 0.02, 22, 3'
   

5. Calculate mean average precision’s layer

   add more top layer parameter (around line 2514), just same as the Cluster bboxes’ layer mentioned in step 3.

   For example, if you have 3 classes, you need to add

   top: 'bbox-list-class0'
   top: 'bbox-list-class1'
   top: 'bbox-list-class3'
   

6. param_str value in Calculate mean average precision’s layer

   change the last parameter of the param_str

   param_str : ‘1920, 1152, 16, NUM_OF_CLASSES’

   param_str : '1920, 1152, 16, 3'
   

7. Final Output Layer

   Insert the number of output layer that match your number of classes. If you have 3 classes, you need to insert 3 set of the
   layers.

   The name inside bottom layer parameter must be same as above

   An example of 1 set of a layer.
   
   #class 0
   layer {
   type: 'Python'
   name: 'score-class0'
   bottom: 'bbox-list-label-class0'
   bottom: 'bbox-list-class0'
   top: 'bbox-list-scored-class0'
   python_param {
       module: 'caffe.layers.detectnet.mean_ap'
       layer: 'ScoreDetections'
   }
   include: { phase: TEST stage: "val" }
   }
   layer {
   type: 'Python'
   name: 'mAP-class0'
   bottom: 'bbox-list-scored-class0'
   top: 'mAP-class0'
   top: 'precision-class0'
   top: 'recall-class0'
   python_param {
       module: 'caffe.layers.detectnet.mean_ap'
       layer: 'mAP'
       param_str : '1920, 1152, 16'
   }
   include: { phase: TEST stage: "val" }
   }