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https://github.com/ternaus/retinaface
The remake of the https://github.com/biubug6/Pytorch_Retinaface
https://github.com/ternaus/retinaface
deeplearning face-detection object-detection pytorch retinaface
Last synced: about 2 months ago
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The remake of the https://github.com/biubug6/Pytorch_Retinaface
- Host: GitHub
- URL: https://github.com/ternaus/retinaface
- Owner: ternaus
- License: mit
- Created: 2020-07-19T21:08:51.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2023-01-27T01:15:53.000Z (over 1 year ago)
- Last Synced: 2024-05-01T15:48:54.035Z (5 months ago)
- Topics: deeplearning, face-detection, object-detection, pytorch, retinaface
- Language: Python
- Homepage:
- Size: 317 KB
- Stars: 377
- Watchers: 16
- Forks: 107
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# Retinaface
[](https://zenodo.org/badge/latestdoi/280950959)
This repo is build on top of [https://github.com/biubug6/Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface)
## Differences
### Train loop moved to [Pytorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning)
IT added a set of functionality:
* Distributed training
* fp16
* Syncronized BatchNorm
* Support for various loggers like [W&B](https://www.wandb.com/) or [Neptune.ml](https://neptune.ai/)
### Hyperparameters are defined in the config file
Hyperparameters that were scattered across the code moved to the config at [retinadace/config](retinadace/config)
### Augmentations => [Albumentations](https://albumentations.ai/)
Color that were manually implemented replaced by the Albumentations library.
Todo:
* Horizontal Flip is not implemented in Albumentations
* Spatial transforms like rotations or transpose are not implemented yet.
Color transforms defined in the config.
### Added mAP calculation for validation
In order to track the progress, mAP metric is calculated on validation.
## Installation
`pip install -U retinaface_pytorch`
## Example inference
```python
import cv2
from retinaface.pre_trained_models import get_model
```
`image = `
```python
model = get_model("resnet50_2020-07-20", max_size=2048)
model.eval()
annotation = model.predict_jsons(image)
```
* Jupyter notebook with the example: [](https://colab.research.google.com/drive/1wLXZyoybDRKizfcIzxPwkeYp-XDpTM-K?usp=sharing)
* Jupyter notebook with the example on how to combine face detector with mask detector: [](https://colab.research.google.com/drive/13Ktsrx164eQHfDmYLyMCoI-Kq0gC5Kg1?usp=sharing)
## Data Preparation
The pipeline expects labels in the format:
```
[
{
"file_name": "0--Parade/0_Parade_marchingband_1_849.jpg",
"annotations": [
{
"bbox": [
449,
330,
571,
720
],
"landmarks": [
[
488.906,
373.643
],
[
542.089,
376.442
],
[
515.031,
412.83
],
[
485.174,
425.893
],
[
538.357,
431.491
]
]
}
]
},
```
You can convert the default labels of the WiderFaces to the json of the propper format with this [script](https://github.com/ternaus/iglovikov_helper_functions/blob/master/iglovikov_helper_functions/data_processing/wider_face/prepare_data.py).
## Training
### Install dependencies
```
pip install -r requirements.txt
pip install -r requirements_dev.txt
```
### Define config
Example configs could be found at [retinaface/configs](retinaface/configs)
### Define environmental variables
```bash
export TRAIN_IMAGE_PATH=
export VAL_IMAGE_PATH=
export TRAIN_LABEL_PATH=
export VAL_LABEL_PATH=
```
### Run training script
```
python retinaface/train.py -h
usage: train.py [-h] -c CONFIG_PATH
optional arguments:
-h, --help show this help message and exit
-c CONFIG_PATH, --config_path CONFIG_PATH
Path to the config.
```
## Inference
```
python retinaface/inference.py -h
usage: inference.py [-h] -i INPUT_PATH -c CONFIG_PATH -o OUTPUT_PATH [-v]
[-g NUM_GPUS] [-m MAX_SIZE] [-b BATCH_SIZE]
[-j NUM_WORKERS]
[--confidence_threshold CONFIDENCE_THRESHOLD]
[--nms_threshold NMS_THRESHOLD] -w WEIGHT_PATH
[--keep_top_k KEEP_TOP_K] [--world_size WORLD_SIZE]
[--local_rank LOCAL_RANK] [--fp16]
optional arguments:
-h, --help show this help message and exit
-i INPUT_PATH, --input_path INPUT_PATH
Path with images.
-c CONFIG_PATH, --config_path CONFIG_PATH
Path to config.
-o OUTPUT_PATH, --output_path OUTPUT_PATH
Path to save jsons.
-v, --visualize Visualize predictions
-g NUM_GPUS, --num_gpus NUM_GPUS
The number of GPUs to use.
-m MAX_SIZE, --max_size MAX_SIZE
Resize the largest side to this number
-b BATCH_SIZE, --batch_size BATCH_SIZE
batch_size
-j NUM_WORKERS, --num_workers NUM_WORKERS
num_workers
--confidence_threshold CONFIDENCE_THRESHOLD
confidence_threshold
--nms_threshold NMS_THRESHOLD
nms_threshold
-w WEIGHT_PATH, --weight_path WEIGHT_PATH
Path to weights.
--keep_top_k KEEP_TOP_K
keep_top_k
--world_size WORLD_SIZE
number of nodes for distributed training
--local_rank LOCAL_RANK
node rank for distributed training
--fp16 Use fp6
```
```
python -m torch.distributed.launch --nproc_per_node= retinaface/inference.py
```
* [Weights](https://drive.google.com/drive/folders/1DuiwlTd1BbZ0ZzafrV7qMncko1Z5a412?usp=sharing) for the model with [config](retinaface/configs/2020-07-19.yaml).
* [Weights](https://drive.google.com/file/d/1slNNW1bntYqDKpvi2r1QfcQAwnhsVw9n/view?usp=sharing) for the model with [config](retinaface/configs/2020-07-20.yaml).
# Web app
https://retinaface.herokuapp.com/
Code for the web app: https://github.com/ternaus/retinaface_demo
### Converting to ONNX
The inference could be sped up on CPU by converting the model to ONNX.
```
Ex: python -m converters.to_onnx -m 1280 -o retinaface1280.onnx
```