tqtg / zalo-landmark

Zalo AI Challenge - Landmark Identification

Zalo AI Challenge - Landmark Identification

This code is ranked top-1 Final Leaderboard of the challenge.

Required Packages

$ pip3 install -r requirements.txt

Data

Data Preparation

Let's assume that the provided dataset is already in data folder:

• data/train_val2018.zip
• data/train_val2018.json

After extracting train_val2018.zip, we will have TrainVal folder containing all images inside data.

We run the code to prepare data for training:

• train/validation with the ratio of 92%/8%.
• splitting it into 10 folds in the cross-validation manner.

$ python3 src/data_prepare.py

After finished, all needed files are in data folder and ready for training. The class_dis.txt file contains distribution over all classes, which is also created inside data and later used for weighted loss in training.

Data Augmentation

• Random rotation
• Central cropping
• Color distortion
• Aspect distortion
• Random cropping
• Random horizontal flipping

Training

Model Architectures

• Inception-ResNet
• ResNet-152

Other architectures are also available: ResNet-50, ResNet-101, ResNet-200, Inception-V4, DenseNet-161, NASNet, PNASNet with pre-trained weights can be downloaded from Slim package.

Model Training

First, we need to download pre-trained weights of Inception-ResNet and ResNet-152 from Slim.

$ chmod 700 download.sh
$ ./download.sh

All downloaded weights will be stored in pretrained folder.

To trained a new model with Inception-ResNet architecture:

$ python3 src/train.py --net "inception_resnet_v2" --checkpoint_exclude_scopes "InceptionResnetV2/Logits,InceptionResnetV2/AuxLogits" --loss_weighted "linear"

With ResNet-152:

$ python3 src/train.py --net "resnet_v2_152" --checkpoint_exclude_scopes "resnet_v2_152/logits" --loss_weighted "linear"

optional arguments:
  -h, --help                show this help message and exit
  --data_dir                DATA_DIR
                              Path to data folder (default: data)
  --log_dir                 LOG_DIR
                              Path to data folder (default: log)
  --checkpoint_dir          CHECKPOINT_DIR
                              Path to checkpoint folder (default: checkpoints)
  --num_checkpoints         NUM_CHECKPOINTS
                              Number of checkpoints to store (default: 1)
  --num_epochs              NUM_EPOCHS
                              Number of training epochs (default: 50)        
  --num_threads             NUM_THREADS
                              Number of threads for data processing (default: 8)
  --batch_size              BATCH_SIZE
                              Batch Size (default: 16)
  --image_size              IMAGE_SIZE
                              Size of images after pre-processed (default: 299)
  --net                     NETWORK ARCHITECTURE
                              Network architecture to use (default: inception_resnet_v2)
  --learning_rate           LEARNING_RATE
                              Starting learning rate (default: 0.01)       
  --train_mode              TRAINING_MODE
                              Training mode (default: 2)
  --fold                    DATA_FOLD
                              Fold of data to train from (default: -1)   
  --optimizer               OPTIMIZER
                              Weight update rule (default: momentum)    
  --lr_decay_rule           LR_DECAY_RULE
                              Decay rule for learning rate (default: step)    
  --loss_weighted           LOSS_WEIGHTED
                              Cross entropy weighted loss mechanism (default: linear)
  --pretrained_dir          PRETRAINED_DIR
                              Path to pre-trained weights (default: pretrained)
  --trainable_scopes        TRAINABLE_SCOPES
                              Scopes of variables to be fixed (default: None)
  --ignore_mixing_vars      IGNORE_MIXING_VARS
                              When restoring a checkpoint would ignore missing variables (default: False)
  --allow_soft_placement    ALLOW_SOFT_PLACEMENT
                              Allow device soft device placement (default: True)

After training, all models will be stored in checkpoints folder.

Final Ensemble Models

• 2 Inception-ResNet and 1 ResNet-152 models trained on 92% and tested on 8% of data.
• 10 Inception-ResNet models trained on 10 folds of cross-validation.

Prediction

Run predict.sh script with an argument which is the path to the data folder containing images. For example, all test images are in data/test:

$ chmod 700 predict.sh
$ ./predict.sh data/test

The final predictions will be in submission.csv file right under current directory.

The code are partially borrowed from Slim package including model architectures and data augmentation with some modifications.