Eiscue - Object Detection for DSTA TIL 2020

Submission for the DSTA TIL 2020 AI Competition.

Object detection of Fashion Images using the Detectron2 framework.

Datasets

The following additional datasets were utilized in the training of our models:

• DeepFashion2 by Yuying Ge et al., 2019

Installation

To install Detectron2 and its dependencies, refer to the official installation instructions.

Config

Each training run is completely defined by customizable parameters in its configuration file, with a few templates already specified in the configs folder.

For example, all the existing config files train the models with pretrained COCO weights:

• cascade_mask_rcnn.yaml: Cascade Mask R-CNN model with ResNet50 backbone.
• faster_rcnn.yaml: Faster R-CNN model with ResNet50 backbone.
• retinanet.yaml: RetinaNet model with ResNet50 backbone.

Other types of models and their respective configs and pretrained weights can be found in the official Detectron2 Model Zoo.

While you can refer to the config reference for a full list of available parameters and what they mean, I've annotated some of them in the existing configs, and some notable ones to customize are:

• SOLVER.IMS_PER_BATCH: Batch size
• SOLVER.BASE_LR: Base learning rate
• SOLVER.STEPS: The iteration number to decrease learning rate by GAMMA
• SOLVER.MAX_ITER: Total number of training iterations
• SOLVER.CHECKPOINT_PERIOD: Saves checkpoint every number of steps
• INPUT.MIN_SIZE_TRAIN: Image input sizes
• TEST.EVAL_PERIOD: The period (in terms of steps) to evaluate the model during training
• OUTPUT_DIR: Specify output directory to save checkpoints, logs, results etc.

Training

To train on a single gpu:

python train_net.py \
    --config-file configs/cascade_mask_rcnn.yaml \
    OUTPUT_DIR output/cascade  # Specify output directory to save weights, logs etc.

To train on multiple gpus:

python train_net.py \
    --num-gpus 4 \
    --config-file configs/cascade_mask_rcnn.yaml \
    OUTPUT_DIR output/cascade  # Specify output directory to save weights, logs etc.

To resume training from a checkpoint (finds last checkpoint from cfg.OUTPUT_DIR)

python train_net.py \
    --config-file config.yaml \  # Config file of halted run
    --resume

To see all other options:

python train_net.py -h

Evaluation

This command only runs evaluation on the test dataset:

python train_net.py \
    --eval-only \
    --config-file configs/cascade_mask_rcnn.yaml \  # Config file of trained model
    MODEL.WEIGHTS /path/to/checkpoint_file \  # Path to trained checkpoint
    OUTPUT_DIR output/eval  # Specify output directory to save results, predictions etc.