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https://github.com/TreB1eN/InsightFace_Pytorch

Pytorch0.4.1 codes for InsightFace
https://github.com/TreB1eN/InsightFace_Pytorch

Last synced: 16 days ago
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Pytorch0.4.1 codes for InsightFace

Host: GitHub
URL: https://github.com/TreB1eN/InsightFace_Pytorch
Owner: TreB1eN
License: mit
Created: 2018-07-30T22:38:04.000Z (almost 6 years ago)
Default Branch: master
Last Pushed: 2022-11-22T02:49:08.000Z (over 1 year ago)
Last Synced: 2024-02-23T13:33:47.246Z (4 months ago)
Language: Jupyter Notebook
Homepage:
Size: 14 MB
Stars: 1,655
Watchers: 26
Forks: 418
Open Issues: 136
Metadata Files:
- Readme: README.md
- License: LICENSE

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my-awesome-stars - TreB1eN/InsightFace_Pytorch - Pytorch0.4.1 codes for InsightFace (Jupyter Notebook)
awesome-stars - TreB1eN/InsightFace_Pytorch - Pytorch0.4.1 codes for InsightFace (Jupyter Notebook)

README

        # InsightFace_Pytorch

Pytorch0.4.1 codes for InsightFace

------

## 1. Intro

- This repo is a reimplementation of Arcface[(paper)](https://arxiv.org/abs/1801.07698), or Insightface[(github)](https://github.com/deepinsight/insightface)

- For models, including the pytorch implementation of the backbone modules of Arcface and MobileFacenet

- Codes for transform MXNET data records in Insightface[(github)](https://github.com/deepinsight/insightface) to Image Datafolders are provided

- Pretrained models are posted, include the [MobileFacenet](https://arxiv.org/abs/1804.07573) and IR-SE50 in the original paper

------

## 2. Pretrained Models & Performance

[IR-SE50 @ BaiduNetdisk](https://pan.baidu.com/s/12BUjjwy1uUTEF9HCx5qvoQ), [IR-SE50 @ Onedrive](https://1drv.ms/u/s!AhMqVPD44cDOhkPsOU2S_HFpY9dC)

| LFW(%) | CFP-FF(%) | CFP-FP(%) | AgeDB-30(%) | calfw(%) | cplfw(%) | vgg2_fp(%) |

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

| 0.9952 | 0.9962    | 0.9504    | 0.9622      | 0.9557   | 0.9107   | 0.9386     |

[Mobilefacenet @ BaiduNetDisk](https://pan.baidu.com/s/1hqNNkcAjQOSxUjofboN6qg), [Mobilefacenet @ OneDrive](https://1drv.ms/u/s!AhMqVPD44cDOhkSMHodSH4rhfb5u)

| LFW(%) | CFP-FF(%) | CFP-FP(%) | AgeDB-30(%) | calfw(%) | cplfw(%) | vgg2_fp(%) |

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

| 0.9918 | 0.9891    | 0.8986    | 0.9347      | 0.9402   | 0.866    | 0.9100     |

## 3. How to use

- clone

  ```

  git clone https://github.com/TropComplique/mtcnn-pytorch.git

  ```

### 3.1 Data Preparation

#### 3.1.1 Prepare Facebank (For testing over camera or video)

Provide the face images your want to detect in the data/face_bank folder, and guarantee it have a structure like following:

```

data/facebank/

        ---> id1/

            ---> id1_1.jpg

        ---> id2/

            ---> id2_1.jpg

        ---> id3/

            ---> id3_1.jpg

           ---> id3_2.jpg

```

#### 3.1.2 download the pretrained model to work_space/model

If more than 1 image appears in one folder, an average embedding will be calculated

#### 3.2.3 Prepare Dataset ( For training)

download the refined dataset: (emore recommended)

- [emore dataset @ BaiduDrive](https://pan.baidu.com/s/1eXohwNBHbbKXh5KHyItVhQ), [emore dataset @ Dropbox](https://www.dropbox.com/s/wpx6tqjf0y5mf6r/faces_ms1m-refine-v2_112x112.zip?dl=0)

- More Dataset please refer to the [original post](https://github.com/deepinsight/insightface/wiki/Dataset-Zoo)

**Note:** If you use the refined [MS1M](https://arxiv.org/abs/1607.08221) dataset and the cropped [VGG2](https://arxiv.org/abs/1710.08092) dataset, please cite the original papers.

- after unzip the files to 'data' path, run :

  ```

  python prepare_data.py

  ```

  after the execution, you should find following structure:

```

faces_emore/

            ---> agedb_30

            ---> calfw

            ---> cfp_ff

            --->  cfp_fp

            ---> cfp_fp

            ---> cplfw

            --->imgs

            ---> lfw

            ---> vgg2_fp

```

------

### 3.2 detect over camera:

- 1. download the desired weights to model folder:

- [IR-SE50 @ BaiduNetdisk](https://pan.baidu.com/s/12BUjjwy1uUTEF9HCx5qvoQ)

- [IR-SE50 @ Onedrive](https://1drv.ms/u/s!AhMqVPD44cDOhkPsOU2S_HFpY9dC)

- [Mobilefacenet @ BaiduNetDisk](https://pan.baidu.com/s/1hqNNkcAjQOSxUjofboN6qg)

- [Mobilefacenet @ OneDrive](https://1drv.ms/u/s!AhMqVPD44cDOhkSMHodSH4rhfb5u)

- 2 to take a picture, run

  ```

  python take_pic.py -n name

  ```

  press q to take a picture, it will only capture 1 highest possibility face if more than 1 person appear in the camera

- 3 or you can put any preexisting photo into the facebank directory, the file structure is as following:

```

- facebank/

         name1/

             photo1.jpg

             photo2.jpg

             ...

         name2/

             photo1.jpg

             photo2.jpg

             ...

         .....

    if more than 1 image appears in the directory, average embedding will be calculated

```

- 4 to start

  ```

  python face_verify.py 

  ```

- - -

### 3.3 detect over video:

```

```

python infer_on_video.py -f [video file name] -s [save file name]

```

```

the video file should be inside the data/face_bank folder

- Video Detection Demo [@Youtube](https://www.youtube.com/watch?v=6r9RCRmxtHE)

### 3.4 Training:

```

```

python train.py -b [batch_size] -lr [learning rate] -e [epochs]

# python train.py -net mobilefacenet -b 200 -w 4

```

```

## 4. References 

- This repo is mainly inspired by [deepinsight/insightface](https://github.com/deepinsight/insightface) and [InsightFace_TF](https://github.com/auroua/InsightFace_TF)

## PS

- PRs are welcome, in case that I don't have the resource to train some large models like the 100 and 151 layers model

- Email : [email protected]