SmartGaze-model-zoo

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SmartGaze-model-zoo

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SmartGaze's accompanying model training factory

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# SmartGaze-model-zoo

SmartGaze's accompanying model training factory

**This is base on InsightFace. You must read the part "Notes".**

Pytorch0.4.1 codes for InsightFace

------

## Notes

The environment configuration for this project is **extremely outdated**. Using the original project code will result in numerous errors. I have adopted modern frameworks and training methodologies to effectively replace the original training and evaluation code.

If your environment corresponds to:
```
torch                     2.4.1+cu118              pypi_0    pypi
torchaudio                2.4.1+cu118              pypi_0    pypi
torchvision               0.19.1+cu118             pypi_0    pypi
```
or similar/newer versions, **you MUST use the modern training scripts. Otherwise, you will encounter an overwhelming number of errors.**

Furthermore, I have provided two scripts to convert a PyTorch model to an .onnx model, and then to convert an .onnx model to a .rknn model.

However, the original code has been retained for the sake of completeness.

For specific details, please refer to the sections starting from `#### 3.4.2 Modern Training (Recommended)` and `### 3.5 Model Conversion & Deployment (RK3568)`.

> If you find this documentation too cluttered, please view the separate [README_MODERN.md](./README_MODERN.md).
> However, if you want to train either of the two models provided by the project, it is recommended to download the model parameters (model download link) provided by the original author and use them.

这个项目环境配置等**过于古老和落后**，使用原始项目会有许多报错。我在此基础上使用现代化的框架和训练方法，基本上代替了原项目的训练和评估代码。如果你的环境是:
```
torch                     2.4.1+cu118              pypi_0    pypi
torchaudio                2.4.1+cu118              pypi_0    pypi
torchvision               0.19.1+cu118             pypi_0    pypi
```
以上或差不多的版本，**请一定要使用现代的训练脚本进行训练，否则报错的条数远超您的想象**。

并且，我在此基础上提供了将 pytorch 模型转为 .onnx 模型，再将 onnx 转为 rknn 模型的两个脚本。

但为了完整性，仍然保留的原始代码。

具体部分请看：`#### 3.4.2 Modern Training (Recommended)` 以后的部分。
以及### 3.5 Model Conversion & Deployment (RK3568)。

> 如果觉得过于杂乱，跳转到另一[README_MODERN.md](./README_MODERN.md)查看。
> 但是，如果你要训练项目提供的两种模型，建议把原作者提供的模型参数（模型下载链接）下载下来使用

------

## 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:

#### 3.4.1 Traditional Training (Original Script)

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

# Example: Train MobileFaceNet on CASIA-WebFace
python train.py -net mobilefacenet -b 200 -w 4 -d casia-webface -e 20 -s 9981
```

#### 3.4.2 Modern Training (Recommended)

**New training script with improved features:**
- No PIL dependency (uses cv2 only)
- Better progress tracking with tqdm
- Simplified data loading
- TensorBoard integration

**Step 1: Check Dataset**
```bash
# Check if your dataset format is compatible with MobileFaceNet (112x112)
python check_dataset.py
```

**Step 2: Train Model**
```bash
# Basic usage
python train_modern.py -d datasets/casia-webface -b 200 -w 4 -e 20

# With initial step (continue training or adjust step counter)
python train_modern.py -d datasets/casia-webface -b 200 -w 4 -e 20 -s 9981

# Resume from checkpoint
python train_modern.py -d datasets/casia-webface -b 200 -w 4 -e 15 -s 11461 \
    -r work_space/models/mobilefacenet_epoch20_step11461_final.pth
```

**Step 3: Monitor Training**
```bash
# View training curves in TensorBoard
tensorboard --logdir=work_space/log
```

**Training Parameters:**
- `-d, --data_path`: Dataset path (default: `datasets/casia-webface`)
- `-b, --batch_size`: Batch size (default: 200)
- `-e, --epochs`: Number of epochs (default: 10)
- `-lr, --lr`: Learning rate (default: 0.001)
- `-w, --num_workers`: Number of workers (default: 4)
- `-s, --initial_step`: Initial step number for display (default: 0)
- `-r, --resume`: Resume from checkpoint (optional)

**Output:**
- Models saved in: `work_space/models/`
- TensorBoard logs in: `work_space/log/`

---

### 3.5 Model Conversion & Deployment (RK3568)

#### 3.5.1 PyTorch → ONNX Conversion

Convert trained PyTorch model to ONNX format:

```bash
# Convert pretrained model
python convert_to_onnx.py -i mobilefacenet.pth -o mobilefacenet.onnx

# Convert trained model
python convert_to_onnx.py -i work_space/models/mobilefacenet_epoch20_step11461_final.pth \
                          -o mobilefacenet_trained.onnx

# Custom batch size
python convert_to_onnx.py -i mobilefacenet.pth -o mobilefacenet.onnx --batch-size 1
```

**Requirements:**
```bash
pip install onnx onnxruntime
```

**Output:**
- ONNX model with verified accuracy
- Automatic validation against PyTorch output
- Model size: ~4.8 MB

#### 3.5.2 ONNX → RKNN Conversion (For RK3568 NPU)

**⚠️ Must run on Linux (VMware VM recommended)**

**Prerequisites:**
```bash
# Install RKNN-Toolkit2
pip install rknn-toolkit2==2.3.2
```

**Prepare Calibration Dataset:**
```bash
# Create filtered label file (only for int_data folders)
grep -E "casia-webface/000696|casia-webface/000697" \
    datasets/int_data/casia-webface.txt > datasets/int_data/int_data_labels.txt
```

**Convert to RKNN:**
```bash
# With INT8 quantization (recommended, 50 calibration images)
python convert_onnx_to_rknn.py -i mobilefacenet.onnx -o mobilefacenet.rknn

# Custom calibration image count
python convert_onnx_to_rknn.py -i mobilefacenet.onnx -o mobilefacenet.rknn --max-calib-images 100

# Without quantization (FP16, slower but more accurate)
python convert_onnx_to_rknn.py -i mobilefacenet.onnx -o mobilefacenet.rknn --no-quantization

# Custom dataset path
python convert_onnx_to_rknn.py -i mobilefacenet.onnx -o mobilefacenet.rknn \
    -d datasets/int_data -l datasets/int_data/int_data_labels.txt
```

**Conversion Parameters:**
- Target Platform: RK3568
- Quantization: INT8 (w8a8 - 权重8位 + 激活8位)
- Optimization Level: 3
- Preprocessing: RGB, mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5]

**Output:**
- RKNN model: ~1-2 MB (quantized)
- Quantization accuracy loss: ~1-3%

#### 3.5.3 Deploy on RK3568

**Inference Example:**
```python
import cv2
import numpy as np
from rknnlite.api import RKNNLite

# Load model
rknn = RKNNLite()
rknn.load_rknn('mobilefacenet.rknn')
rknn.init_runtime()

# Prepare input (112x112 RGB image, uint8, [0-255])
img = cv2.imread('face.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (112, 112))

# Add batch dimension: (112, 112, 3) -> (1, 112, 112, 3)
img = np.expand_dims(img, axis=0)

# Inference (specify NHWC format)
outputs = rknn.inference(inputs=[img], data_format='nhwc')
embedding = outputs[0][0]  # 512-dim feature vector

# Compare faces (cosine similarity)
similarity = np.dot(embedding1, embedding2)  # threshold: 0.3-0.5

rknn.release()
```

**Input Specification:**
- Size: 112×112
- Format: RGB, HWC (Height, Width, Channels)
- Type: uint8
- Range: [0, 255]
- Preprocessing: Automatically handled by RKNN model

**Output:**
- 512-dimensional feature vector (float32)
- L2-normalized

---

## 4. Project Structure

```
InsightFace_Pytorch/
├── train.py                    # Original training script
├── train_modern.py             # Modern training script (recommended)
├── check_dataset.py            # Dataset format checker
├── convert_to_onnx.py          # PyTorch → ONNX converter
├── convert_onnx_to_rknn.py     # ONNX → RKNN converter (Linux only)
├── model.py                    # Model definitions (MobileFaceNet, IR-SE, ArcFace)
├── config.py                   # Configuration file
├── Learner.py                  # Training learner class
├── data/                       # Data processing utilities
│   └── data_pipe.py
├── mtcnn_pytorch/              # Face detection & alignment
│   └── src/
│       └── align_trans.py      # Face alignment (for deployment)
├── datasets/                   # Training datasets
│   ├── casia-webface/          # Main training dataset
│   └── int_data/               # Calibration dataset for RKNN
│       ├── 000696/
│       ├── 000697/
│       └── int_data_labels.txt
├── work_space/                 # Training outputs
│   ├── models/                 # Saved models
│   └── log/                    # TensorBoard logs
└── mobilefacenet.pth          # Pretrained model
```

---

## 5. References

- This repo is mainly inspired by [deepinsight/insightface](https://github.com/deepinsight/insightface) , [InsightFace_TF](https://github.com/auroua/InsightFace_TF) and [InsightFace_Pytorch]https://github.com/TreB1eN/InsightFace_Pytorch?tab=readme-ov-file

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