Kuzushiji Recognition

Kaggle Kuzushiji Recognition: Code for the 8th place solution.

The kuzushiji recognition pipeline is consists of two models: CenterNet character detection model and MobileNetV3 per-character classification model.

Contents

• Setup
  • Language environment
  • Download dataset
• Training procedure
• Prepare submission
• Python API
• License

Setup

Language environment

Python version:

• 3.7.3

Libraries:

• chainer (6.2.0)
• chainercv (0.13.1)
• cupy-cuda92 (6.2.0)
• albumentations (0.3.1)
• opencv-python (4.1.0.25)
• Pillow (6.1.0)
• pandas (0.25.0)
• numpy (1.17.0)
• matplotlib (3.1.1)
• japanize-matplotlib (1.0.4)

For unittest:

• pytest (4.4.1)

Download dataset

Please download the competition dataset from here and unzip to <repo root>/data/kuzushiji-recognition.

The expected directory structure is as follows:

kuzushiji-recognition/
    data/
        kuzushiji-recognition/
            train.csv
            train_images
            test_images
            unicode_translation.csv
            sample_submission.csv

Training procedure

Please follow the steps below to train kuzushiji recognition models.

1. Set environment variable:

cd <path to this repo>
export PYTHONPATH=`pwd`

2. Split all annotated samples written in train.csv into train and validation split:

python scripts/prepare_train_val_split.py

3. Prepare per-character cropped image set for character classifier training:

python scripts/prepare_char_crop_dataset.py

4. Train character detection model:

python scripts/train_detector.py --gpu 0 --out ./results/detector --full-data

5. Train character classification model:

python scripts/train_classifier.py --gpu 0 --out ./results/classifier --full-data

6. Prepare pseudo label using trained detector and classifier:

python scripts/prepare_pseudo_labels.py --gpu 0 \
    ./results/detector/model_700.npz \
    ./results/classifier/model_900.npz \
    --out data/kuzushiji-recognition-pseudo

7. Finetune classifier using pseudo label and original training data:

python scripts/finetune_classifier.py --gpu 0 \
    --pseudo-labels-dir  data/kuzushiji-recognition-pseudo \
    --out ./results/classifier-finetune \
    ./results/classifier/model_900.npz

Prepare submission

To generate a CSV for submission, please execute the following commands.:

python scripts/prepare_submission.py --gpu 0 \
    ./results/detector/model_700.npz \
    ./results/classifier-finetune/model_100.npz

Python API

The detector class and the classifier class provide easy-to-use inferface for inference. This is an example of inference code. Note that the bounding box format is (xmin, ymin, xmax, ymax).

import chainer
from PIL import Image

from kr.detector.centernet.resnet import Res18UnetCenterNet
from kr.classifier.softmax.mobilenetv3 import MobileNetV3
from kr.datasets import KuzushijiUnicodeMapping


# unicode <-> unicode index mapping
mapping = KuzushijiUnicodeMapping()

# load trained detector
detector = Res18UnetCenterNet()
chainer.serializers.load_npz('./results/detector/model_700.npz', detector)

# load trained classifier
classifier = MobileNetV3(out_ch=len(mapping))
chainer.serializers.load_npz('./results/classifier/model_900.npz', classifier)

# load image
image = Image.open('path/to/image.jpg')

# character detection
bboxes, bbox_scores = detector.detect(image)

# character classification
unicode_indices, scores = classifier.classify(image, bboxes)
unicodes = [mapping.index_to_unicode(idx) for idx in unicode_indices]

License

Released under the MIT license.