https://github.com/markivory2973/facerecognition
Face recognition in PyTorch.
https://github.com/markivory2973/facerecognition
celeba lfw python pytorch shufflenet-v2 shufflenetv2
Last synced: 5 months ago
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Face recognition in PyTorch.
- Host: GitHub
- URL: https://github.com/markivory2973/facerecognition
- Owner: MarkIvory2973
- License: mit
- Created: 2024-08-27T08:46:37.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-01-30T15:21:55.000Z (5 months ago)
- Last Synced: 2025-01-30T16:31:18.608Z (5 months ago)
- Topics: celeba, lfw, python, pytorch, shufflenet-v2, shufflenetv2
- Language: Python
- Homepage:
- Size: 48.5 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# FaceRecognition
Face recognition in PyTorch.
## Installation
Create an environment (Python **3.10**):
```bash
conda create -n FaceRecognition python=3.10
```
Install dependencies:
- For CPU:
```bash
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cpu
```
- For AMD GPU (for **Windows** users):
```bash
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cpu
pip3 install torch-directml
```
- For NVIDIA GPU:
```bash
pip install torch torchvision --index-url https://download.pytorch.org/whl/cuxxx
```
```bash
pip install opencv-python rich
```
Clone this repository:
```bash
git clone https://github.com/MarkIvory2973/ProxyTest.git
```
## Usage
```bash
cd FaceRecognition
python src/main.py train -d E:/Datasets -c ./checkpoints -e 40 -r 0.02 -s 16 -g 0.94
python src/main.py register -i 0 -c ./checkpoints -n Guest01
python src/main.py verify -i 0 -d E:/Datasets -c ./checkpoints -n Guest01
```
## Parameters
Train mode:
|Parameter|Required|Default|Description|
|:-|:-:|:-|:-|
|--datasets-root,-d|✓|-|Datasets root|
|--checkpoints-root,-c|-|./checkpoints|Checkpoints root|
|--total-epoch,-e|-|100|Total epoch|
|--learning-rate,-r|-|0.02|Learning rate|
|--batch-size,-s|-|16|Batch size|
|--gamma,-g|-|0.98|The gamma of ExponentialLR|
Register mode:
|Parameter|Required|Default|Description|
|:-|:-:|:-|:-|
|--camera-id,-i|-|0|Camera ID|
|--checkpoints-path,-c|-|./checkpoints|Checkpoints path|
|--username,-n|✓|-|Username|
Verify mode:
|Parameter|Required|Default|Description|
|:-|:-:|:-|:-|
|--camera-id,-i|-|0|Camera ID|
|--datasets-root,-d|✓|-|Datasets root|
|--checkpoints-path,-c|-|./checkpoints|Checkpoints path|
|--username,-n|✓|-|Username|
## Result
⚠ The metrics in `checkpoint.N.pth` are based on the output of **each 16 batch**, so their `Accuracy` may be not correct enough (**lower than actual**).
I tested again with `batch_size=1`. Here is the result of `checkpoint.best.pth` (`checkpoint.100.pth`):
||Accuracy (%)|
|:-:|-:|
|LFW|94.03|
## References
1. [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://arxiv.org/abs/1503.03832)
2. [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)