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https://github.com/peteryuX/retinaface-tf2

RetinaFace (Single-stage Dense Face Localisation in the Wild, 2019) implemented (ResNet50, MobileNetV2 trained on single GPU) in Tensorflow 2.0+. This is an unofficial implementation. With Colab.
https://github.com/peteryuX/retinaface-tf2

colab colab-notebook face-detection facedetection mobilenetv2 resnet-50 retinaface retinaface-detector tensorflow tensorflow2 tf2

Last synced: about 2 months ago
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RetinaFace (Single-stage Dense Face Localisation in the Wild, 2019) implemented (ResNet50, MobileNetV2 trained on single GPU) in Tensorflow 2.0+. This is an unofficial implementation. With Colab.

Host: GitHub
URL: https://github.com/peteryuX/retinaface-tf2
Owner: peteryuX
License: mit
Created: 2020-02-01T08:36:01.000Z (almost 5 years ago)
Default Branch: master
Last Pushed: 2023-03-24T22:36:41.000Z (over 1 year ago)
Last Synced: 2024-08-01T03:17:21.487Z (4 months ago)
Topics: colab, colab-notebook, face-detection, facedetection, mobilenetv2, resnet-50, retinaface, retinaface-detector, tensorflow, tensorflow2, tf2
Language: Python
Homepage:
Size: 1.27 MB
Stars: 269
Watchers: 12
Forks: 97
Open Issues: 27
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

awesome-tensorflow-2 - RetinaFace: Single-stage Dense Face Localisation in the Wild

README

        # [retinaface-tf2](https://github.com/peteryuX/retinaface-tf2)

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[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/peteryuX/retinaface-tf2/blob/master/notebooks/colab-github-demo.ipynb)

:fire: RetinaFace (RetinaFace: Single-stage Dense Face Localisation in the Wild, published in 2019) implemented (ResNet50, MobileNetV2 trained on single GPU) in Tensorflow 2.0+. This is an unofficial implementation. :fire:

> RetinaFace presents a robust single-stage face detector, which performs pixel-wise face localisation on various scales of faces by taking advantages of joint extra-supervised and self-supervised multi-task learning(manually annotate five facial landmarks on the WIDER FACE). On the WIDER FACE hard test set, RetinaFace outperforms the state of the art average precision (AP) by 1.1% (achieving AP equal to 91.4%).

Original Paper:   [Arxiv](https://arxiv.org/abs/1905.00641)

Offical Implementation:   [MXNet](https://github.com/deepinsight/insightface/tree/master/RetinaFace)

:: Results from this reporepository. ::



****

## Contents

:bookmark_tabs:

* [Installation](#Installation)

* [Data Preparing](#Data-Preparing)

* [Training and Testing](#Training-and-Testing)

* [Benchmark](#Benchmark)

* [Models](#Models)

* [References](#References)

***

## Installation

:pizza:

Create a new python virtual environment by [Anaconda](https://www.anaconda.com/) or just use pip in your python environment and then clone this repository as following.

### Clone this repo

```bash

git clone https://github.com/peteryuX/retinaface-tf2.git

cd retinaface-tf2

```

### Conda

```bash

conda env create -f environment.yml

conda activate retinaface-tf2

```

### Pip

```bash

pip install -r requirements.txt

```

****

## Data Preparing

:beer:

**Step 1**: Download the [WIDER FACE](http://shuoyang1213.me/WIDERFACE/index.html) dataset images from the download links bellow.

| Dataset Name | Link |

|:------------:|:----------:|

| WIDER Face Training Images | [Google Drive](https://drive.google.com/file/d/0B6eKvaijfFUDQUUwd21EckhUbWs/view?usp=sharing) |

| WIDER Face Validation Images | [Google Drive](https://drive.google.com/file/d/0B6eKvaijfFUDd3dIRmpvSk8tLUk/view?usp=sharing) |

**Step 2**: Download the [Retinaface official annotations](https://github.com/deepinsight/insightface/tree/master/RetinaFace#Data) (face bounding boxes & five facial landmarks) from the download links bellow.

| Dataset Name | Link |

|:------------:|:----------:|

| Retinaface Annotations | [Google Drive](https://drive.google.com/file/d/1vgCABX1JI3NGBzsHxwBXlmRjaLV3NIsG/view?usp=sharing) / [Dropbox](https://www.dropbox.com/s/7j70r3eeepe4r2g/retinaface_gt_v1.1.zip?dl=0) |

**Step 3**: Extract downloaded files into `./data/widerface/`. The directory structure should be like bellow.

```

./data/widerface/

    train/

        images/

        label.txt

    val/

        images/

        label.txt

```

**Step 4**: Convert the training images and annotations to tfrecord file with the the script bellow.

```bash

# Binary Image (recommend): need additional space

python data/convert_train_tfrecord.py --output_path="./data/widerface_train_bin.tfrecord" --is_binary=True

# Online Image Loading:

python data/convert_train_tfrecord.py --output_path="./data/widerface_train.tfrecord" --is_binary=False

```

Note:

- You can run `python ./dataset_checker.py` to check if the dataloader work. Please modify the inside setting (in [./dataset_checker.py](https://github.com/peteryuX/retinaface-tf2/blob/master/dataset_checker.py)) for different situations.

****

## Training and Testing

:lollipop:

### Config File

You can modify your own dataset path or other settings of model in [./configs/*.yaml](https://github.com/peteryuX/retinaface-tf2/tree/master/configs) for training and testing, which like below.

```python

# general setting

batch_size: 8

input_size: 640

backbone_type: 'ResNet50'  # 'ResNet50', 'MobileNetV2'

sub_name: 'retinaface_res50'

# training dataset

dataset_path: './data/widerface_train_bin.tfrecord'

dataset_len: 12880  # number of training samples

using_bin: True

using_flip: True

using_distort: True

# testing dataset

testing_dataset_path: './data/widerface/val'

# network

out_channel: 256

# anchor setting

min_sizes: [[16, 32], [64, 128], [256, 512]]

steps: [8, 16, 32]

match_thresh: 0.45

ignore_thresh: 0.3

variances: [0.1, 0.2]

clip: False

# training setting

epoch: 100

init_lr: !!float 1e-2

lr_decay_epoch: [50, 68]

lr_rate: 0.1

warmup_epoch: 5

min_lr: !!float 1e-3

weights_decay: !!float 5e-4

momentum: 0.9

pretrain: True

save_steps: 2000

```

Note:

- The `sub_name` is the name of outputs directory used in checkpoints and logs folder. (make sure of setting it unique to other models)

- The `using_bin` is used to choose the type of training data, which should be according to the data type you created in the [Data-Preparing](#Data-Preparing).

- The `save_steps` is the number interval steps of saving checkpoint file.

### Training

Train the Retinaface model by yourself, or dowload it from [BenchmarkModels](#Models).

```bash

# train ResNet50 backbone model

python train.py --cfg_path="./configs/retinaface_res50.yaml" --gpu=0

# or train MobileNetV2 backbone model

python train.py --cfg_path="./configs/retinaface_mbv2.yaml" --gpu=0

```

Note:

- The `--gpu` is used to choose the id of your avaliable GPU devices with `CUDA_VISIBLE_DEVICES` system varaible.

- You can visualize the learning rate scheduling by running "`python ./modules/lr_scheduler.py`".

### Testing on [WIDER FACE](http://shuoyang1213.me/WIDERFACE/index.html) Validation Set

You can download my trained models for testing from [Models](#Models) without training it yourself. And, evaluate the models you got with the corresponding cfg file on the testing dataset.

**Step 1**: Produce txt results and visualizations from model.

```bash

# Test ResNet50 backbone model

python test_widerface.py --cfg_path="./configs/retinaface_res50.yaml" --gpu=0

# or

# Test ResNet50 backbone model

python test_widerface.py --cfg_path="./configs/retinaface_mbv2.yaml" --gpu=0

```

Note:

- The visualizations results would be saved into `./results/`.

**Step 2**: Evaluate txt results. (Codes come from [Here](https://github.com/wondervictor/WiderFace-Evaluation))

```bash

cd ./widerface_evaluate

python setup.py build_ext --inplace

python evaluation.py

```

### Detect on Input Image

You can detect on your image by the model. For example, detect on the image from [./data/0_Parade_marchingband_1_149.jpg](https://github.com/peteryuX/retinaface-tf2/blob/master/data/0_Parade_marchingband_1_149.jpg) as following.

```bash

python test.py --cfg_path="./configs/retinaface_res50.yaml" --img_path="./data/0_Parade_marchingband_1_149.jpg" --down_scale_factor=1.0

# or

python test.py --cfg_path="./configs/retinaface_mbv2.yaml" --img_path="./data/0_Parade_marchingband_1_149.jpg" --down_scale_factor=1.0

```

Note:

- You can down scale your input by the `--down_scale_factor`.

### Demo on Webcam

Demo face detection on your webcam.

```bash

python test.py --cfg_path="./configs/retinaface_res50.yaml" --webcam=True --down_scale_factor=1.0

# or

python test.py --cfg_path="./configs/retinaface_mbv2.yaml" --webcam=True --down_scale_factor=1.0

```

****

## Benchmark

:coffee:

### WiderFace Validation Set Performance in single scale

| Model Name          | Easy   | Medium | Hard   |

|---------------------|--------|--------|--------|

| Tensorflow2 MobileNetV2 | 93.23% | 91.74% | 80.01% |

| Tensorflow2 ResNet50 | 94.21% | 93.25% | 83.55% |

| [Pytorch ResNet50](https://github.com/biubug6/Pytorch_Retinaface) | 95.48% | 94.04% | 84.43% |

| [Mxnet ResNet50 (Official)](https://github.com/deepinsight/insightface/tree/master/RetinaFace) | 94.97% | 93.89% | 82.27% |

****

Note:

- Tensorflow2 version (ResNet50/MobileNetV2) in this repository only trained with batch size (8/16) on single GPU.

- All models evaluate on original image scale.

## Models

:doughnut:

| Model Name          | Download Link |

|---------------------|---------------|

| Retinaface ResNet50 | [GoogleDrive](https://drive.google.com/file/d/1qnuci2BVUNH3fqVqQYCoub4Wd2gShld9/view?usp=sharing) |

| Retinaface MobileNetV2 | [GoogleDrive](https://drive.google.com/file/d/16HBH2bpSY3TQ_STryWFe72CIcUzp6GRy/view?usp=sharing) |

Note:

- After dowloading these models, extract them into `./checkpoints` for restoring.

- All training settings of the models can be found in the corresponding [./configs/*.yaml](https://github.com/peteryuX/retinaface-tf2/tree/master/configs) files.

- You can see the tensorboard traning logs in [here](https://tensorboard.dev/experiment/2Jr2OUjpS2CWr9SySPK2tQ/#scalars).

- **Based on the property of the training dataset, all the pre-trained models can only be used for non-commercial applications.**

****

## References

:hamburger:

Thanks for these source codes porviding me with knowledges to complete this repository.

- https://github.com/deepinsight/insightface/tree/master/detection/retinaface (Official)

    - Face Analysis Project on MXNet http://insightface.ai

- https://github.com/biubug6/Pytorch_Retinaface

    - Retinaface get 80.99% in widerface hard val using mobilenet0.25.

- https://github.com/wondervictor/WiderFace-Evaluation

    - Python Evaluation Code for Wider Face Dataset

- https://github.com/zzh8829/yolov3-tf2

    - YoloV3 Implemented in TensorFlow 2.0