How to test MNIST with example images?

[petewarden]

I'm working on an O'Reilly webcast focused on getting started with Caffe:
http://www.oreilly.com/pub/e/3121

One of my goals is to show folks how to quickly train their own simple network, and I'm able to walk them through training MNIST. The hard part is demonstrating how to use the resulting network on an image.

I modified python/classifier.py to optionally output human-readable scores, which I've been able to use successfully with the prebuilt Imagenet. It's been a struggle to get the same thing working with MNIST though. I've made a bunch of changes so it can handle single-channel grayscale inputs, but I still can't get workable results. The example digit images I try appear to result in random predictions.

Here's my fork, just containing changes in the Python scripts to support MNIST:
https://github.com/jetpacapp/caffe

To run the prediction on a digit image, use this command:
python python/classify.py --print_results --model_def examples/mnist/lenet.prototxt --pretrained_model examples/mnist/lenet_iter_10000 --force_grayscale --center_only --labels_file data/mnist/mnist_words.txt data/mnist/sample_digit.png foo

I'd hoped to see labels corresponding to the digits in the images I put in, but in fact I seem to see fairly arbitrary results.

Any ideas on how to accomplish my goal of demonstrating MNIST on user-provided images?

[shelhamer]

You might look at the changes in dev for grayscale processing to compare
against your work.

I'll take a glance at your fork if I can -- keep up your efforts as I know
there are groups working on handwriting and scene text recognition and
other intensity image data with success.

Le vendredi 18 juillet 2014, Pete Warden notifications@github.com a
écrit :

I'm working on an O'Reilly webcast focused on getting started with Caffe:
http://www.oreilly.com/pub/e/3121

One of my goals is to show folks how to quickly train their own simple
network, and I'm able to walk them through training MNIST. The hard part is
demonstrating how to use the resulting network on an image.

I modified python/classifier.py to optionally output human-readable
scores, which I've been able to use successfully with the prebuilt
Imagenet. It's been a struggle to get the same thing working with MNIST
though. I've made a bunch of changes so it can handle single-channel
grayscale inputs, but I still can't get workable results. The example digit
images I try appear to result in random predictions.

Here's my fork, just containing changes in the Python scripts to support
MNIST:
https://github.com/jetpacapp/caffe

To run the prediction on a digit image, use this command:
python python/classify.py --print_results --model_def
examples/mnist/lenet.prototxt --pretrained_model
examples/mnist/lenet_iter_10000 --force_grayscale --center_only
--labels_file data/mnist/mnist_words.txt data/mnist/sample_digit.png foo

I'd hoped to see labels corresponding to the digits in the images I put
in, but in fact I seem to see fairly arbitrary results.

Any ideas on how to accomplish my goal of demonstrating MNIST on
user-provided images?

—
Reply to this email directly or view it on GitHub
#729.

Evan Shelhamer

[petewarden]

After digging deeper, the problem was the default image dimensions of 256x256, since MNIST crops to 28x28, so only the very center of each image was being analyzed. Once I figured that out, I was able to get the right results with this modified command-line on my fork:

python python/classify.py --print_results --model_def examples/mnist/lenet.prototxt --pretrained_model examples/mnist/lenet_iter_10000 --force_grayscale --center_only --labels_file data/mnist/mnist_words.txt --images_dim 28,28 data/mnist/sample_digit.png foo

Since it looks like my modifications do allow python/classify.py to work with MNIST as well as Imagenet, I'll put together a pull request from my fork. Having them in the main project would be a big help for the tutorial, that way I can direct folks here rather than messing around with forks.

[petewarden]

I've added the pull request as #735 .

[shelhamer]

Thanks for holding the Caffe webinar and the extension to classify.py for grayscale inputs and human-readable output. Closing since this is carried out by #816 taking over for #735.

[fromgop]

python python/classify.py --print_results --model_def examples/mnist/lenet.prototxt --pretrained_model examples/mnist/lenet_iter_10000 --force_grayscale --center_only --labels_file data/mnist/mnist_words.txt --images_dim 28,28 data/mnist/sample_digit.png foo
in the path what is this foo?

[jenifferYingyiWu]

I have trained and tested my image datasets using Caffe, and the data in val.txt is the ground truth.    
But, how can I calculate the precision and recall by the ground truth and prediction values?

Or, can I get the precision and recall from "python/classify.py"?
Could you pls give me some suggestions and recommend some python codes?

[kishankedia]

I tried running the command that you have suggested but i am getting an error saying that
Exception: Channel swap needs to have the same number of dimensions as the input channels. I am passing an RGB image as input as --force_graysclale is there it should convert that image to a single channel but it seems it is performing that . Can you suggest any other alternatives ?

[9crk]

it's hard for new users to finish this work.

I did it , checkout this: https://github.com/9crk/caffe-mnist-test