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By Garvin Li
The Image classification by Tensorflow section introduces how to use the TensorFlow framework of deep learning to classify CIFAR-10 images. This section introduces another deep learning framework: Caffe. With Caffe, you can complete image classification model training by editing configuration files.
This experiment uses a CIFAR-10 open-source dataset, containing 60,000 images with pixel dimensions 32 x 32. These images are classified into 10 categories: airplanes, automobiles, birds, cats, deer. dogs, frogs, horses, ships, and trucks. The following figure shows the dataset.
The dataset has already been stored in the public dataset in Alibaba Cloud Machine Learning Platform for AI in JPG format. Machine learning users can directly enter the following paths in the Data Source Path field of deep learning components:
Enter the path, as shown in the following figure:
The Caffe framework of deep learning currently only supports certain formats. Therefore, you must first use the format conversion component to convert the JPG images.
After format conversion, the following files are generated in the output OSS path, including a piece of training data and a piece of testing data.
Record the corresponding paths for editing the Net file. The following is an example of the data paths:
Enter the preceding paths in the Net file, as follows:
Edit the Solver file:
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