See the reference implementation at http://fcn.berkeleyvision.org. This pre-release is deprecated.
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Save shelhamer/80667189b218ad570e82 to your computer and use it in GitHub Desktop.
I was able to get a mean 53.5% accuracy after 10150 iteration with a loss of 67968. This was with a 50/50 random split for train vs test. The accuracy was determined by adding an 'accuracy' layer to train_val.prototxt. How is the mean I/U determined? Is there a similar layer that can be added?
Why the learning rate is so small, can anyone explain it,plz?
Which should I use to train my own data,the solver.py or the solver.prototxt? I used the solver.prototxt to train my data,but the loss didn't decrease. Dose anyone have a solution? Thanks
Hi, anyone know where to download "vgg16fc.caffemodel" in the solver.py?
I would like to share my script for creating a custom data set in lmdb format. It would be very appreciated if you could let me know when you find any bug on this code.
#!/usr/bin/python
import caffe
import glob
import lmdb
import numpy as np
from PIL import Image
import os
import sys
# Variables
img_width = 500
img_height = 500
# Paths
# PNG images
color_dir = './input/color_image_dir'
# PNG images
# Per-pixel labels are stored in a gray image
label_dir = './input/label_image_dir'
output_dir = './lmdb/'
inputs = glob.glob(color_dir + '/*.png')
color_lmdb_name = output_dir + '/color-lmdb'
if not os.path.isdir(color_lmdb_name):
os.makedirs(color_lmdb_name)
color_in_db = lmdb.open(color_lmdb_name, map_size=int(1e12))
label_lmdb_name = output_dir + '/label-lmdb'
if not os.path.isdir(label_lmdb_name):
os.makedirs(label_lmdb_name)
label_in_db = lmdb.open(label_lmdb_name, map_size=int(1e12))
num_images = 0;
color_mean_color = np.zeros((3))
with color_in_db.begin(write=True) as color_in_txn:
with label_in_db.begin(write=True) as label_in_txn:
for in_idx, in_ in enumerate(inputs):
img_name = os.path.splitext( os.path.basename(in_))[0]
color_filename = color_dir + img_name + '.png'
label_filename = label_dir + img_name + '.png'
print(str(in_idx + 1) + ' / ' + str(len(inputs)))
# load image
im = np.array(Image.open(color_filename)) # or load whatever ndarray you need
assert im.dtype == np.uint8
# RGB to BGR
im = im[:,:,::-1]
# in Channel x Height x Width order (switch from H x W x C)
im = im.transpose((2,0,1))
# compute mean color image
for i in range(3):
color_mean_color[i] += im[i,:,:].mean()
num_images += 1
#color_im_dat = caffe.io.array_to_datum(im)
color_im_dat = caffe.proto.caffe_pb2.Datum()
color_im_dat.channels, color_im_dat.height, color_im_dat.width = im.shape
assert color_im_dat.height == img_height
assert color_im_dat.width == img_width
color_im_dat.data = im.tostring()
color_in_txn.put('{:0>12d}'.format(in_idx), color_im_dat.SerializeToString())
im = np.array(Image.open(label_filename)) # or load whatever ndarray you need
assert im.dtype == np.uint8
label_im_dat = caffe.proto.caffe_pb2.Datum()
label_im_dat.channels = 1
label_im_dat.height, label_im_dat.width = im.shape
assert label_im_dat.height == img_height
assert label_im_dat.width == img_width
label_im_dat.data = im.tostring()
label_in_txn.put('{:0>12d}'.format(in_idx), label_im_dat.SerializeToString())
label_in_db.close()
color_in_db.close()
color_mean_color /= num_images
np.savetxt(output_dir + '/{}.csv'.format('color-mean'), color_mean_color, delimiter=",", fmt='%.4f')
pad: 100 in conv1_1 layer is wrong..
When I tried to run deploy.prototxt, it takes very large memory, and the process crash on CPU.
Can anyone help me to reduce the use of memory or solve this problem?
Thanks in advance.
Hi guys,
how to finetune on different number of classes?
thanks
Can I fine-tune this model for high-resolution image classification? The input sizes of Alexnet/Caffenet/GoogleLenet are too small for my application.
@weiliu89 Hi, I encountered conflicts when I merged PR #2016 , it says "Automatic merge failed". What should I do next? Thank you in advance.
Hi, I tried to use deconv layer with group and bilinear as upsampling instead of using the solver script, but could hardly reproduce the result. Anybody knows the reason?
@acgtyrant
Hi I am getting this error :
I0421 02:38:28.223543 24891 net.cpp:299] Memory required for data: 1277452160
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:505] Reading dangerously large protocol message. If the message turns out to be larger than 2147483647 bytes, parsing will be halted for security reasons. To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:78] The total number of bytes read was 597011289
I installed the protobuf using the command given in google tensorflow website
$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/protobuf-3.0.0b2.post2-cp27-none-linux_x86_64.whl
However, once I uninstalled that and tried to compile using the github source code from google protobuf, python can't find the google protobuf at all. Can you help me finding a way here?
Thanks in advance
@yjc04
I had same error. I just modified "kDefaultTotalBytesLimit" and "kDefaultTotalBytesWarningThreshold" in the "/usr/include/google/protobuf/io/coded_stream.h". I didn't recompile and reinstall, it works well.
Hello all, Have anyone try to export prediction image using C++, instead of python? I am not the family of python code. Thank all
any c++ for classification or Segmentation would be great.
Hi,
I trained the FCN32 from the scratch. But the output is zero values (a black image). Could someone help what is the reason? Thanks
What is the train/val split on the PASCAL Context data that is needed to get the 35.1 mean I/U?
There is a train.txt and val.txt in the VOC2010/ImageSets/Segmentation folder of the PASCAL VOC 2010 download, but these only have 964 images in each. The Long et al. paper seems to use half of the 10,103 images available for training and the other half for testing. So, I don't think these are the correct files. The PASCAL Context Dataset download also doesn't have any files for the train/val split.