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Fashionista

Model architecture:

Mask R-CNN model

SpineNet-143 + FPN backbone

Training:

Pre-trained on the COCO dataset

Image resolution: 1280x1280

Focal loss for the attributes head

Augmentations: random scaling (0.5x - 2.0x), v3 policy from the AutoAugment (modified to support masks)

Setup

https://github.com/tensorflow/tpu/tree/master/tools/ctpu#download

  • Copy and modify a TPU config file

cp tpu_configs/example.json tpu_configs/YOUR_TPU.json

  • Create a VM instance and TPU
export TPU_CONFIG_JSON=tpu_configs/YOUR_TPU.json
./scripts/tpu.sh create

You will automatically log in the VM via SSH.

cd $HOME
git clone https://github.com/amitha-nayak/Fashionista.git
cd $HOME/kaggle-imaterialist2020-model
./scripts/setup_bashrc.sh
source ~/.bashrc
./scripts/install_requirements.sh
  • Setup is done. Log out from the terminal, and stop the VM and TPU.
./scripts/tpu.sh stop

# or
./scripts/tpu.sh stop --vm
./scripts/tpu.sh stop --tpu

Train

  • Log in to the VM instance via SSH.
./scripts/tpu.sh start
./scripts/tpu.sh ssh
  • Set up iMaterialist dataset.
  • Create TF Records from iMaterialist COCO format annotations
poetry shell

./scripts/create_tf_records.sh \
    train \
    $IMAGE_DIR \
    $COCO_JSON_FILE \
    $OUTPUT_FILE_PREFIX

# e.g.,
./scripts/create_tf_records.sh \
    train \
    ~/iMaterialist/raw/train \
    ~/iMaterialist/raw/instances_attributes_train2020.json \
    gs://yourbucket/tfrecords/train

TF Records will be created like gs://yourbucket/tfrecords/train-00001-of-00050.tfrecord.

  • Train a model.
./scripts/train.sh $INPUT_GCS_PATTERN $OUT_GCS_DIR

# e.g.,
./scripts/train.sh \
    gs://yourbucket/tfrecords/train-* \
    gs://yourbucket/model

Training artifacts (checkpoints, hyperparmeters, and logs etc) will be dumped into gs://yourbucket/model/.

  • Don't forget to stop your TPU if the training finishes.
./scripts/tpu.sh stop --tpu
  • Note: If the training fails, delete the training artifacts from GCS. Otherwise, the configurations of the failed training will be loaded and it will fail again. For example, tensor's shape mismatch.

Predict

  • Log in to the VM instance via SSH.
./scripts/tpu.sh start --vm
./scripts/tpu.sh ssh
  • Predict for your images
poetry shell

./scripts/predict.sh \
    $MODEL_GCS_DIR \
    $IMAGE_GCS_DIR \
    $TF_RECORD_GCS_DIR \
    $OUT_GCS_DIR

# e.g.,
./scripts/predict.sh \
    gs://yourbucket/model \
    gs://yourbucket/yourdataset/images \
    gs://yourbucket/yourdataset/tfrecords \
    gs://yourbucket/yourdataset/predictions
  • The TPU should be automatically shut down by scripts/predict.sh.

Django UI

  • Log in to the VM instance via SSH.
./scripts/tpu.sh start --vm
./scripts/tpu.sh ssh
  • Run django
cd kaggle-imaterialist2020-model
python3 ../manage.py runserver
  • Don't forget to stop your TPU.
./scripts/tpu.sh stop --tpu
  • Note: to change or add sample images on the website,

use the path ./fash/media/images

All the changes were made on top of the TPU Object Detection and Segmentation Framework.