Caffe net.predict() outputs random results (GoogleNet)

问题:

I used pretrained GoogleNet from https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet and finetuned it with my own data (~ 100k images, 101 classes). After one day training I achieved 62% in top-1 and 85% in top-5 classification and try to use this network to predict several images.

I just followed example from https://github.com/BVLC/caffe/blob/master/examples/classification.ipynb,

Here is my Python code:

import caffe import numpy as np   caffe_root = './caffe'   MODEL_FILE = 'caffe/models/bvlc_googlenet/deploy.prototxt' PRETRAINED = 'caffe/models/bvlc_googlenet/bvlc_googlenet_iter_200000.caffemodel'  caffe.set_mode_gpu()  net = caffe.Classifier(MODEL_FILE, PRETRAINED,                mean=np.load('ilsvrc_2012_mean.npy').mean(1).mean(1),                channel_swap=(2,1,0),                raw_scale=255,                image_dims=(224, 224))  def caffe_predict(path):         input_image = caffe.io.load_image(path)         print path         print input_image         prediction = net.predict([input_image])           print prediction         print "----------"          print 'prediction shape:', prediction[0].shape         print 'predicted class:', prediction[0].argmax()           proba = prediction[0][prediction[0].argmax()]         ind = prediction[0].argsort()[-5:][::-1] # top-5 predictions           return prediction[0].argmax(), proba, ind 

In my deploy.prototxt I changed the last layer only to predict my 101 classes.

layer {   name: "loss3/classifier"   type: "InnerProduct"   bottom: "pool5/7x7_s1"   top: "loss3/classifier"   param {     lr_mult: 1     decay_mult: 1   }   param {     lr_mult: 2     decay_mult: 0   }   inner_product_param {     num_output: 101     weight_filler {       type: "xavier"     }     bias_filler {       type: "constant"       value: 0     }   } } layer {   name: "prob"   type: "Softmax"   bottom: "loss3/classifier"   top: "prob" } 

Here is the distribution of softmax output:

[[ 0.01106235  0.00343131  0.00807581  0.01530041  0.01077161  0.0081002    0.00989228  0.00972753  0.00429183  0.01377776  0.02028225  0.01209726    0.01318955  0.00669979  0.00720005  0.00838189  0.00335461  0.01461464    0.01485041  0.00543212  0.00400191  0.0084842   0.02134697  0.02500303    0.00561895  0.00776423  0.02176422  0.00752334  0.0116104   0.01328687    0.00517187  0.02234021  0.00727272  0.02380056  0.01210031  0.00582192    0.00729601  0.00832637  0.00819836  0.00520551  0.00625274  0.00426603    0.01210176  0.00571806  0.00646495  0.01589645  0.00642173  0.00805364    0.00364388  0.01553882  0.01549598  0.01824486  0.00483241  0.01231962    0.00545738  0.0101487   0.0040346   0.01066607  0.01328133  0.01027429    0.01581303  0.01199994  0.00371804  0.01241552  0.00831448  0.00789811    0.00456275  0.00504562  0.00424598  0.01309276  0.0079432   0.0140427    0.00487625  0.02614347  0.00603372  0.00892296  0.00924052  0.00712763    0.01101298  0.00716757  0.01019373  0.01234141  0.00905332  0.0040798    0.00846442  0.00924353  0.00709366  0.01535406  0.00653238  0.01083806    0.01168014  0.02076091  0.00542234  0.01246306  0.00704035  0.00529556    0.00751443  0.00797437  0.00408798  0.00891858  0.00444583]] 

It seems just like random distribution with no sense.

Thank you for any help or hint and best regards, Alex

回答1:

The solution is really simple: I just forgot to rename the last layer in deploy file:

layer {   name: "loss3/classifier"   type: "InnerProduct"   bottom: "pool5/7x7_s1"   top: "loss3/classifier"   param {     lr_mult: 1     decay_mult: 1   } 

回答2:

Please check the image transformation you are using - is it the same in training and test-time?

AFAIK bvlc_googlenet subtract image mean with one value per channel, while your python classifier uses different mean mean=np.load('ilsvrc_2012_mean.npy').mean(1).mean(1). This might cause the net to be unable to classify your inputs correctly.

文章来源: Caffe net.predict() outputs random results (GoogleNet)