Alibaba Cloud Machine Learning Platform for AI: Image Classification by Caffe

This article introduces Caffe deep learning framework to perform complete image classification model training on Alibaba Cloud's Machine Learning Platform for AI.

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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.

Make sure that you have already read the Deep Learning section and activated deep learning in Alibaba Cloud Machine Learning Platform for AI (PAI).

Datasets

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:

Testing data: oss://dl-images.oss-cn-shanghai-internal.aliyuncs.com/cifar10/caffe/images/cifar10_test_image_list.txt
Training data: oss://dl-images.oss-cn-shanghai-internal.aliyuncs.com/cifar10/caffe/images/cifar10_train_image_list.txt

Enter the path, as shown in the following figure:

Format Conversion

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.

OSS Path Storing Images and Table Files: set this parameter to the path of the public dataset predefined in Alibaba Cloud Machine Learning Platform for AI.
Output OSS Path: user-defined OSS path.

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:

Training data data_file_list.txt: bucket/cifar/train/data_file_list.txt
Training data: data_mean.binaryproto:bucket/cifar/train/data_mean.binaryproto
Testing data data_file_list.txt: bucket/cifar/test/data_file_list.txt
Testing data: data_mean.binaryproto:bucket/cifar/test/data_mean.binaryproto

Caffe Configuration Files

Enter the preceding paths in the Net file, as follows:

Edit the Solver file:

Run the Experiment

Upload the Solver and Net files to OSS, drag and drop the Caffe component to the canvas, and connect the component to the data source.
Set the parameters in the Caffe component, as shown in the following figure. Set the Solver OSS Path to the OSS path of the uploaded Solver file and then click Run.
View the generated image classification model file in the model storage path on OSS. You can use the following models to classify images.
To view the corresponding log, refer to Logview in Image classification by TensorFlow.

Community

Alibaba Cloud Machine Learning Platform for AI: Image Classification by Caffe

Datasets

Format Conversion

Caffe Configuration Files

Run the Experiment

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