detection

Object Detection Models on TensorFlow 2.0

Note: The repo is still under construction. More features and instructions will be added soon.

Prerequsite

To get started, make sure to use Tensorflow 2.1+ on Google Cloud. Also here are a few package you need to install to get started:

sudo apt-get install -y python-tk && \
pip install Cython matplotlib opencv-python-headless pyyaml Pillow && \
pip install 'git+https://github.com/cocodataset/cocoapi#egg=pycocotools&subdirectory=PythonAPI'

Next, download the code from TensorFlow models github repository or use the pre-installed Google Cloud VM.

git clone https://github.com/tensorflow/models.git

Train RetinaNet on TPU

Train a vanilla ResNet-50 based RetinaNet.

TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
RESNET_CHECKPOINT="<path to the pre-trained Resnet-50 checkpoint>"
TRAIN_FILE_PATTERN="<path to the TFRecord training data>"
EVAL_FILE_PATTERN="<path to the TFRecord validation data>"
VAL_JSON_FILE="<path to the validation annotation JSON file>"
python ~/models/official/vision/detection/main.py \
  --strategy_type=tpu \
  --tpu="${TPU_NAME?}" \
  --model_dir="${MODEL_DIR?}" \
  --mode=train \
  --params_override="{ type: retinanet, train: { checkpoint: { path: ${RESNET_CHECKPOINT?}, prefix: resnet50/ }, train_file_pattern: ${TRAIN_FILE_PATTERN?} }, eval: { val_json_file: ${VAL_JSON_FILE?}, eval_file_pattern: ${EVAL_FILE_PATTERN?} } }"

Train a custom RetinaNet using the config file.

First, create a YAML config file, e.g. my_retinanet.yaml. This file specifies the parameters to be overridden, which should at least include the following fields.

# my_retinanet.yaml
type: 'retinanet'
train:
  train_file_pattern: <path to the TFRecord training data>
eval:
  eval_file_pattern: <path to the TFRecord validation data>
  val_json_file: <path to the validation annotation JSON file>

Once the YAML config file is created, you can launch the training using the following command.

TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
python ~/models/official/vision/detection/main.py \
  --strategy_type=tpu \
  --tpu="${TPU_NAME?}" \
  --model_dir="${MODEL_DIR?}" \
  --mode=train \
  --config_file="my_retinanet.yaml"

Train RetinaNet on GPU

Note: Instructions are comming soon.

References

1. Focal Loss for Dense Object Detection. Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. IEEE International Conference on Computer Vision (ICCV), 2017.