pinpom/xception-with-your-own-dataset

Easy-to-use scripts for training and inferencing with Xception on your own dataset

Training Xception with your own dataset

Description

This repository contains some scripts to train Xception introduced by François Chollet, the founder of Keras.

Environments

We tested our scripts in the following environment.

• GTX1070 (8GB) A middle-range or more powerful GPU is required.
• python 3.6.5
• numpy 1.17.4
• scipy 1.3.3
• h5py 2.10.0
• Keras 2.3.1
• tensorflow-gpu 1.15.0

Demo

Here, we'll show how to train Xception on the Caltech101 dataset (9145 images, 102 classes) as an example.

1. Prepare dataset

Please download and expand the dataset with the following command.

$ sh download_dataset.sh
$ tar zxvf 101_ObjectCategories.tar.gz

2. Make classes.txt

You must create a text file where all the class names are listed line by line.
This can be easily done with the below command.

$ ls 101_ObjectCategories > classes.txt

3. Train the model

$ python fine_tune.py 101_ObjectCategories/ classes.txt result/

In fine_tune.py...

• Xception's weights are initialized with the ones pre-trained on the ImageNet dataset (officialy provided by the keras team).
• In the first training stage, only the top classifier of the model is trained for 5 epochs.
• In the second training stage, the whole model is trained for 50 epochs with a lower learning rate.
• All the result data (serialized model files and figures) are to be saved under result/

4. Inference

$ python inference.py result/model_fine_final.h5 classes.txt images/airplane.jpg

[Input Image]:

[Output Result]:

How to train with your own dataset ?

What do you have to prepare ?

1. A dataset you wanna use

You have to prepare a directory which has the same structure as the caltech101 dataset as shown bellow:

The above example dataset has 3 classes and 5 images in total. Each class name must be unique, but the image files' can be anything.

2. classes.txt

You have to create a text file where all the class names are listed line by line. This can be done with the following command.

$ ls root/ > classes.txt

The file name does not need to be classes.txt, but you can name it as you want.

Let's train your model on your own dataset !!

$ python fine_tune.py root/ classes.txt <result_root> [epochs_pre] [epochs_fine] [batch_size_pre] [batch_size_fine] [lr_pre] [lr_fine] [snapshot_period_pre] [snapshot_period_fine]

NOTE: [] indicates an optional argument. <> indicates a required argument.

• <result_root>: Path to the directory where all the result data will be saved.
• [epochs_pre]: The number of epochs during the first training stage (default: 5).
• [epochs_fine]: The number of epochs during the second training stage (default: 50).
• [batch_size_pre]: Batch size during the first training stage (default: 32).
• [batch_size_fine]: Batch size during the second training stage (default: 16).
• [lr_pre]: Learning rate during the first training stage (default:1e-3).
• [lr_fine]: Learning rate during the second training stage (default:1e-4).
• [snapshot_period_pre]: Snapshot period during the first training stage (default:1). At the every spedified epochs, a serialized model file will be saved under <result_root>.
• [snapshot_period_fine]: Snapshot period during the second training stage (default:1).

For example, if you'd like to pre-train a model for 2 epochs with leraning rate 5e-3 and fine-tune it for 10 epochs with learning rate 5e-4, please run the following command.

$ python fine_tune.py root/ classes.txt result/ --epochs_pre 2 --epochs_fine 10 --lr_pre 5e-3 --lr_fine 5e-4

How to inference with your trained model ?

$ python inference.py <model> <classes> <image> [top_n]

NOTE: [] indicates an optional argument. <> indicates a required argument.

• <model>: Path to a serialized model file.
• <classes>: Path to a txt file where all the class names are listed line by line.
• <image>: Path to an image file that you would like to classify.
• [top_n]: Show top n results (default: 10).