The MNIST is a large database of handwritten digits commonly used for training various image processing systems. This section provides a comparison of Caffe and TensorFlow models for Handwritten Digit Recognition. The data set used for these applications is from Yann Lecun. This is an MNIST data set sample:
Setting Up the Board
Step 1 - Create the following folder and grant it permission as it follows:
NOTE: The argument 10 refers to the number of predictions for each test.
These tests run the inference on the input MNIST dataset images (Actual), showing the inference results (Predict) and how long it took to complete the prediction. The input images for this test are in the binary form and can be found at the t10k-images-idx3-ubyte.gz package from Yann Lecun.
By the output results, it is possible to notice that the Caffe model is slower than TensorFlow, however, it is also more accurate than the latter. Change the argument to compare further results between the two models.