在前一篇随笔中，数据制作成了VOC2007格式，可以用于Faster-RCNN的训练。
1.针对数据的修改
修改datasets\VOCdevkit2007\VOCcode\VOCinit.m，我只做了两类
VOCopts.classes={...
'dog'
'flower'};
修改function\fast_rcnn\fast_rcnn_train.m，val_iters不能大于val数据量（我的只有几十个）。
ip.addParamValue('val_iters', 20, @isscalar);
修改function\rpn\proposal_train.m，与上一致。
ip.addParamValue('val_iters', 20, @isscalar);
修改models\fast_rcnn_prototxts中两个文件夹里面的train_val.prototxt和test.prototxt，以K代表类别数做相应的修改，（共4个文件修改12处）。
input: "bbox_targets"
input_dim: 1 # to be changed on-the-fly to match num ROIs
input_dim: 12 # 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: 12 # 4 * (K+1) (=21) classes
input_dim: 1
input_dim: 1
type: "InnerProduct"
inner_product_param {
num_output: 3 #K+1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
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: 3 # K+1
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: 12 # 4 * (K+1)
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0
}
}
}
修改experiments\+Model\ZF_for_Faster_RCNN_VOC2007.m的三个为solver_30k40k.prototxt，默认60k80k所需时间过长。
2.根据设备性能的修改
显卡GTX750，显存2G，尽管数据不多，在默认设置下出现了内存不够的错误。
修改functions\fast_rcnn\fast_rcnn_config.m，以%标注的为默认值。
%% training
% whether use gpu
ip.addParamValue('use_gpu', gpuDeviceCount > 0, ...
@islogical);
% Image scales -- the short edge of input image
ip.addParamValue('scales', 60, @ismatrix); %600
% Max pixel size of a scaled input image
ip.addParamValue('max_size', 1000, @isscalar);
% Images per batch
ip.addParamValue('ims_per_batch', 2, @isscalar);
% Minibatch size
ip.addParamValue('batch_size', 32, @isscalar); %128
% Fraction of minibatch that is foreground labeled (class > 0)
ip.addParamValue('fg_fraction', 0.25, @isscalar);
% Overlap threshold for a ROI to be considered foreground (if >= fg_thresh)
ip.addParamValue('fg_thresh', 0.5, @isscalar);
% Overlap threshold for a ROI to be considered background (class = 0 if
% overlap in [bg_thresh_lo, bg_thresh_hi))
ip.addParamValue('bg_thresh_hi', 0.5, @isscalar);
ip.addParamValue('bg_thresh_lo', 0.1, @isscalar);
% mean image, in RGB order
ip.addParamValue('image_means', 128, @ismatrix);
% Use horizontally-flipped images during training?
ip.addParamValue('use_flipped', true, @islogical);
% Vaild training sample (IoU > bbox_thresh) for bounding box regresion
ip.addParamValue('bbox_thresh', 0.5, @isscalar); % random seed
ip.addParamValue('rng_seed', 6, @isscalar); %% testing
ip.addParamValue('test_scales', 60, @isscalar); %600
ip.addParamValue('test_max_size', 1000, @isscalar);
ip.addParamValue('test_nms', 0.3, @isscalar);
ip.addParamValue('test_binary', false, @islogical);
3.开始训练
训练前删除或备份output，imdb\cache，运行experiments/script_faster_rcnn_VOC2007_ZF.m 开始训练。
在我的显卡上经过四个小时，训练完成。
下面是未删除output重新运行（很快）的结果。
***************
stage one proposal
***************
aver_boxes_num = 1090, select top 2000
aver_boxes_num = 1091, select top 2000 ***************
stage one fast rcnn
***************
!!! dog : 0.8969 0.9418
!!! flower : 0.9006 0.9458 ~~~~~~~~~~~~~~~~~~~~
Results:
89.6920
90.0606 89.8763 ~~~~~~~~~~~~~~~~~~~~ ***************
stage two proposal
***************
aver_boxes_num = 1263, select top 2000
aver_boxes_num = 1271, select top 2000 ***************
stage two fast rcnn
*************** ***************
final test
***************
aver_boxes_num = 233, select top 300
!!! dog : 0.8893 0.9449
!!! flower : 0.8990 0.9445 ~~~~~~~~~~~~~~~~~~~~
Results:
88.9304
89.9025 89.4165 ~~~~~~~~~~~~~~~~~~~~
Cleared 0 solvers and 2 stand-alone nets
please modify detection_test.prototxt file for sharing conv layers with proposal model (delete layers until relu5)
>>
4.测试
训练结束已有提示，要先修改detection_test.prototxt。
修改data为1*256*50*50，去掉roi_pool5之前的layer并将bottom改为data。
name: "Zeiler_conv5" input: "data"
input_dim: 1
input_dim: 256
input_dim: 50
input_dim: 50 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 layer {
bottom: "data"
bottom: "rois"
top: "pool5"
name: "roi_pool5"
type: "ROIPooling"
roi_pooling_param {
pooled_w: 6
pooled_h: 6
spatial_scale: 0.0625 # (1/16)
}
}
在experiments\script_faster_rcnn_demo.m中将路径更改成本地相应路径，根据测试结果可以修改thres值。
model_dir = fullfile(pwd, 'output', 'faster_rcnn_final', 'faster_rcnn_VOC2007_ZF'); %% ZF_test
im_names = {'000001.jpg','000002.jpg','000034.jpg','000212.jpg','000213.jpg', '001150.jpg'};
thres = 0.3; %0.6
检测速度很快，不过此次我的数据检测效果很不好，可能由于数据太少、画框不认真或某些没有意识到的参数错误-_-!。