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README.md

Object Detection Models on TensorFlow 2

WARNING: This repository will be deprecated and replaced by the solid implementations inside vision/beta/.

Prerequsite

To get started, download the code from TensorFlow models GitHub repository or use the pre-installed Google Cloud VM.

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

Next, 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 && \
pip3 install -r ~/models/official/requirements.txt

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>"
python3 ~/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?} } }"

The pre-trained ResNet-50 checkpoint can be downloaded here.

Note: The ResNet implementation under detection/ is currently different from the one under classification/, so the checkpoints are not compatible. We will unify the implementation soon.

Train a SpineNet-49 based RetinaNet.

TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
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>"
python3 ~/models/official/vision/detection/main.py \
  --strategy_type=tpu \
  --tpu="${TPU_NAME?}" \
  --model_dir="${MODEL_DIR?}" \
  --mode=train \
  --params_override="{ type: retinanet, architecture: {backbone: spinenet, multilevel_features: identity}, spinenet: {model_id: 49}, 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>"
python3 ~/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

Training on GPU is similar to that on TPU. The major change is the strategy type (use "mirrored" for multiple GPU and "one_device" for single GPU).

Multi-GPUs example (assuming there are 8GPU connected to the host):

MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
  --strategy_type=mirrored \
  --num_gpus=8 \
  --model_dir="${MODEL_DIR?}" \
  --mode=train \
  --config_file="my_retinanet.yaml"
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
  --strategy_type=one_device \
  --num_gpus=1 \
  --model_dir="${MODEL_DIR?}" \
  --mode=train \
  --config_file="my_retinanet.yaml"

An example with inline configuration (YAML or JSON format):

python3 ~/models/official/vision/detection/main.py \
  --model_dir=<model folder> \
  --strategy_type=one_device \
  --num_gpus=1 \
  --mode=train \
  --params_override="eval:
 eval_file_pattern: <Eval TFRecord file pattern>
 batch_size: 8
 val_json_file: <COCO format groundtruth JSON file>
predict:
 predict_batch_size: 8
architecture:
 use_bfloat16: False
train:
 total_steps: 1
 batch_size: 8
 train_file_pattern: <Eval TFRecord file pattern>
use_tpu: False
"

Train Mask R-CNN on TPU

Train a vanilla ResNet-50 based Mask R-CNN.

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>"
python3 ~/models/official/vision/detection/main.py \
  --strategy_type=tpu \
  --tpu=${TPU_NAME} \
  --model_dir=${MODEL_DIR} \
  --mode=train \
  --model=mask_rcnn \
  --params_override="{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} } }"

The pre-trained ResNet-50 checkpoint can be downloaded here.

Note: The ResNet implementation under detection/ is currently different from the one under classification/, so the checkpoints are not compatible. We will unify the implementation soon.

Train a SpineNet-49 based Mask R-CNN.

TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
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>"
python3 ~/models/official/vision/detection/main.py \
  --strategy_type=tpu \
  --tpu="${TPU_NAME?}" \
  --model_dir="${MODEL_DIR?}" \
  --mode=train \
  --model=mask_rcnn \
  --params_override="{architecture: {backbone: spinenet, multilevel_features: identity}, spinenet: {model_id: 49}, train_file_pattern: ${TRAIN_FILE_PATTERN?} }, eval: { val_json_file: ${VAL_JSON_FILE?}, eval_file_pattern: ${EVAL_FILE_PATTERN?} } }"

Train a custom Mask R-CNN using the config file.

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

# my_maskrcnn.yaml
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>"
python3 ~/models/official/vision/detection/main.py \
  --strategy_type=tpu \
  --tpu=${TPU_NAME} \
  --model_dir=${MODEL_DIR} \
  --mode=train \
  --model=mask_rcnn \
  --config_file="my_maskrcnn.yaml"

Train Mask R-CNN on GPU

Training on GPU is similar to that on TPU. The major change is the strategy type (use "mirrored" for multiple GPU and "one_device" for single GPU).

Multi-GPUs example (assuming there are 8GPU connected to the host):

MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
  --strategy_type=mirrored \
  --num_gpus=8 \
  --model_dir=${MODEL_DIR} \
  --mode=train \
  --model=mask_rcnn \
  --config_file="my_maskrcnn.yaml"
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
  --strategy_type=one_device \
  --num_gpus=1 \
  --model_dir=${MODEL_DIR} \
  --mode=train \
  --model=mask_rcnn \
  --config_file="my_maskrcnn.yaml"

An example with inline configuration (YAML or JSON format):

python3 ~/models/official/vision/detection/main.py \
  --model_dir=<model folder> \
  --strategy_type=one_device \
  --num_gpus=1 \
  --mode=train \
  --model=mask_rcnn \
  --params_override="eval:
 eval_file_pattern: <Eval TFRecord file pattern>
 batch_size: 8
 val_json_file: <COCO format groundtruth JSON file>
predict:
 predict_batch_size: 8
architecture:
 use_bfloat16: False
train:
 total_steps: 1000
 batch_size: 8
 train_file_pattern: <Eval TFRecord file pattern>
use_tpu: False
"

Train ShapeMask on TPU

Train a ResNet-50 based ShapeMask.

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>"
SHAPE_PRIOR_PATH="<path to shape priors>"
python3 ~/models/official/vision/detection/main.py \
  --strategy_type=tpu \
  --tpu=${TPU_NAME} \
  --model_dir=${MODEL_DIR} \
  --mode=train \
  --model=shapemask \
  --params_override="{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} } shapemask_head: {use_category_for_mask: true, shape_prior_path: ${SHAPE_PRIOR_PATH}} }"

The pre-trained ResNet-50 checkpoint can be downloaded here.

The shape priors can be downloaded [here] (https://storage.googleapis.com/cloud-tpu-checkpoints/shapemask/kmeans_class_priors_91x20x32x32.npy)

Train a custom ShapeMask using the config file.

First, create a YAML config file, e.g. my_shapemask.yaml. This file specifies the parameters to be overridden:

# my_shapemask.yaml
train:
  train_file_pattern: <path to the TFRecord training data>
  total_steps: <total steps to train>
  batch_size: <training batch size>
eval:
  eval_file_pattern: <path to the TFRecord validation data>
  val_json_file: <path to the validation annotation JSON file>
  batch_size: <evaluation batch size>
shapemask_head:
  shape_prior_path: <path to shape priors>

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>"
python3 ~/models/official/vision/detection/main.py \
  --strategy_type=tpu \
  --tpu=${TPU_NAME} \
  --model_dir=${MODEL_DIR} \
  --mode=train \
  --model=shapemask \
  --config_file="my_shapemask.yaml"

Train ShapeMask on GPU

Training on GPU is similar to that on TPU. The major change is the strategy type (use "mirrored" for multiple GPU and "one_device" for single GPU).

Multi-GPUs example (assuming there are 8GPU connected to the host):

MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
  --strategy_type=mirrored \
  --num_gpus=8 \
  --model_dir=${MODEL_DIR} \
  --mode=train \
  --model=shapemask \
  --config_file="my_shapemask.yaml"

A single GPU example

MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
  --strategy_type=one_device \
  --num_gpus=1 \
  --model_dir=${MODEL_DIR} \
  --mode=train \
  --model=shapemask \
  --config_file="my_shapemask.yaml"

An example with inline configuration (YAML or JSON format):

python3 ~/models/official/vision/detection/main.py \
  --model_dir=<model folder> \
  --strategy_type=one_device \
  --num_gpus=1 \
  --mode=train \
  --model=shapemask \
  --params_override="eval:
 eval_file_pattern: <Eval TFRecord file pattern>
 batch_size: 8
 val_json_file: <COCO format groundtruth JSON file>
train:
 total_steps: 1000
 batch_size: 8
 train_file_pattern: <Eval TFRecord file pattern>
use_tpu: False
"

Run the evaluation (after training)

python3 /usr/share/models/official/vision/detection/main.py \
   --strategy_type=tpu \
   --tpu=${TPU_NAME} \
   --model_dir=${MODEL_DIR} \
   --mode=eval \
   --model=shapemask \
   --params_override="{eval: { val_json_file: ${VAL_JSON_FILE}, eval_file_pattern: ${EVAL_FILE_PATTERN}, eval_samples: 5000 } }"

MODEL_DIR needs to point to the trained path of ShapeMask model. Change strategy_type=mirrored and num_gpus=1 to run on a GPU.

Note: The JSON groundtruth file is useful for COCO dataset and can be downloaded from the COCO website. For custom dataset, it is unncessary because the groundtruth can be included in the TFRecord files.

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.