I want to train tesseract 4.0.0. But when i searched for it, only training for version 3 is seen. Can someone suggest me some blogs that explains tesseract 4.0.0 training.
For training Neural net based LSTM Tesseract 4.0:
https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract-4.00
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Newbie to Caffe.
I am trying to use the trained Convolutional neural network on MNIST dataset using Caffe deep learning framework.
Following the official tutorial.
Steps taken successfully:
./data/mnist/get_mnist.sh
./examples/mnist/create_mnist.sh
./examples/mnist/train_lenet.sh
Model was trained and stopped with the following message:
I1203 solver.cpp:133] Snapshotting solver state to lenet_iter_10000.solverstate
I1203 solver.cpp:78] Optimization Done.
Now, I am not sure as how to get a testing image and use the existing trained model which I believe has been snapshot by the name lenet_iter_10000.solverstate to see the predicted scores for each class.
Use the test function of caffe:
<path to caffe root>/caffe test -model <val filename>.prototxt -weights lenet_iter_10000.caffemodel
As you want to test only one image, give that image as input to your test data layer. Use the mean_image as input as well in your <val filename>.protoxt. Test batch size is 1 in this case.
Also note that lenet_iter_10000.solverstate is not your trained model. Your trained model is actually lenet_iter_10000.caffemodel. To know about the diffrence between solverstate and caffemodel files see here.
I have a general question concerning seq2seq models. There are lots of Open Source Tools like TensorFlow, Torch and others. But I did not find an answer for my question:
Is it possible to add training data to a once trained model without starting the whole training process from the beginning?
Is it possible for me to directly give an image and its segmentation as the input for training a caffe?
Does the original implementation support this?
If yes where can I find an appropreate prototxt file?
Yes. It is possible.
Have a look at Fully Convolutional Networks for Semantic Segmentation and SegNet. They are both fully convolutional networks and are trained for semantic segmentation. The prototxt and caffemodel files are available on GitHub.
You can run FCN with the original implementation, but SegNet uses some layers which are not part of the original implementation. They have an edited version of caffe on Github, so you can use that.
I'm using caffe to train FCNs8 for semantic segmentation task on pascal voc 2012 dataset from scratch, the train_val prototxt is given by caffe model zoo,
but no matter how I tried, adjust learning rate try other learning method, I always get the zero labels for all pixel which stand for background.
The output is all zero which stand for background
Have anyone meet this problem when you are training a segmentation network from scratch?
I also try to train other segmentation networks, such as ParseNet, SegNet from scratch, but the result are also all zero.
I trained on pascal voc2012 as well as augmented pascal dataset.
How can I train and test a CNN using Matlab?
Can anybody give me an example code?
you can find Matlab examples in the Caffe repository: https://github.com/BVLC/caffe/tree/master/matlab
Try this simple tutorial. it contains sample code
https://ahmedibrahimvt.wordpress.com/2016/04/18/caffe-in-matlab-matcaffe-simple-tutorial/