# 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.

```bash
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:

```bash
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.

```bash
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/legacy/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](https://storage.cloud.google.com/cloud-tpu-checkpoints/model-garden-vision/detection/resnet50-2018-02-07.tar.gz).

Note: The ResNet implementation under
[detection/](https://github.com/tensorflow/models/tree/master/official/legacy/detection)
is currently different from the one under
[classification/](https://github.com/tensorflow/models/tree/master/official/vision/image_classification),
so the checkpoints are not compatible.
We will unify the implementation soon.


### Train a SpineNet-49 based RetinaNet.

```bash
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/legacy/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.

```YAML
# 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.

```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/legacy/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](https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy)" for multiple GPU and
"[one_device](https://www.tensorflow.org/api_docs/python/tf/distribute/OneDeviceStrategy)" for single GPU).

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

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

```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/legacy/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/legacy/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.

```bash
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/legacy/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](https://storage.cloud.google.com/cloud-tpu-checkpoints/model-garden-vision/detection/resnet50-2018-02-07.tar.gz).

Note: The ResNet implementation under
[detection/](https://github.com/tensorflow/models/tree/master/official/legacy/detection)
is currently different from the one under
[classification/](https://github.com/tensorflow/models/tree/master/official/vision/image_classification),
so the checkpoints are not compatible.
We will unify the implementation soon.


### Train a SpineNet-49 based Mask R-CNN.

```bash
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/legacy/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.

```YAML
# 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.

```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/legacy/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](https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy)"
for multiple GPU and
"[one_device](https://www.tensorflow.org/api_docs/python/tf/distribute/OneDeviceStrategy)"
for single GPU).

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

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

```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/legacy/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/legacy/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.

```bash
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/legacy/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](https://storage.cloud.google.com/cloud-tpu-checkpoints/model-garden-vision/detection/resnet50-2018-02-07.tar.gz).

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:

```YAML
# 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.

```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/legacy/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](https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy)"
for multiple GPU and
"[one_device](https://www.tensorflow.org/api_docs/python/tf/distribute/OneDeviceStrategy)"
for single GPU).

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

```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/legacy/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

```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/legacy/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/legacy/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/legacy/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](http://cocodataset.org/#home) and can be
downloaded from the [COCO website](http://cocodataset.org/#download). For custom dataset, it is unncessary because the groundtruth can be included in the TFRecord files.

## References

1.  [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002).
    Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. IEEE
    International Conference on Computer Vision (ICCV), 2017.