ZaloAiLandMark

Public source code of top5 ZaloAI LandMark challenge. Pretrained networks could be download in here This tutorial consists of 4 parts:

• Dependencies
• Data processing.
• Training.
• Prediction.

I. Dependencies

• Python 3.5.2
• Keras 2.2.0
• Tensorflow-gpu 1.4.1
• Opencv 3.4.1
• Numpy 1.14.5 All the packages are built on Linux.

II. Data processing

1. Overview

• Clean the dataset (remove junk file, error file, etc).
• Splits the training dataset into 2 sets:

• Training set (90% of the dataset).
• Validation set (10% of the dataset).

2. Coding implementation

• To clean the dataset, we run: python3 prepare_image.py

• Set up the data link in line 9.

• To splits the dataset, we run: python3 splitdata_python.py

III. Training

1. Overview

• This model is a cascade model of 4 networks: InceptionResnetV2, Xception, Resnet50 and Resnet101 network.
• In all the networks, we used pretrained model based on Imagenet classification.
• Some data augmentation techniques have been using:

• rescale=1/255
• rotation_range=20
• width_shift_range=0.2
• height_shift_range=0.2
• shear_range=0.2
• zoom_range=0.2
• horizontal_flip=True
• fill_mode='nearest'

• It took 36 hours for each sub-model (~20 epochs) training on one NVIDIA GeForce GTX 1080 Ti.

2. Coding implementation

• To train the InceptionResnetV2/Xception/Resnet101 network, we run: python3 train_inceptionresnet.py/train_xception.py/train_resnet50.py /train_resnet101.py with some modifications:

• Load pretrained model: • train_inceptionresnet.py: Line 85. • train_xception.py: Line 85. • train_resnet50.py: Line 85. • train_resnet101.py: Line 168.
• Load dataset: • train_inceptionresnet.py: Line 18, 19. • train_xception.py: Line 18, 19. • train_resnet50.py: Line 18, 19. • train_resnet101.py: Line 126, 127.

IV. Prediction

1. Overview

• At first, we run the prediction separately on 3 networks, which creates 3 numpy prediction matrix.
• Then, we take the sum and calculate the final result (top-3 predictions) using softmax.

2. Coding implementation

• To get the prediction matrix of 3 networks, we run: • python3 prediction_inceptionresnet.py • python3 prediction_xception.py • python3 prediction_resnet50.py • python3 prediction_resnet101.py with some modifications:

• Load trained model: • prediction_inceptionresnet.py: Line 32. • prediction_xception.py: Line 32. • prediction_resnet50.py: Line 32. • prediction_resnet101.py: Line 127.
• Load test dataset: • prediction_inceptionresnet.py: Line 37. • prediction_xception.py: Line 37. • prediction_resnet50.py: Line 37. • prediction_resnet101.py: Line 131.

• To get the final result csv file in the result section, we run: python3 predict_cascade.py