https://github.com/tejasvi541/face-recognition-siamese
Facial Recognition using the Siamese advance network
https://github.com/tejasvi541/face-recognition-siamese
deep-learning docker tensorflow
Last synced: 2 months ago
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Facial Recognition using the Siamese advance network
- Host: GitHub
- URL: https://github.com/tejasvi541/face-recognition-siamese
- Owner: tejasvi541
- Created: 2022-03-20T05:46:27.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-03-20T06:20:41.000Z (over 4 years ago)
- Last Synced: 2025-01-12T23:08:11.800Z (over 1 year ago)
- Topics: deep-learning, docker, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 9.77 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Face Recognition using Siamese Network
### Dataset - http://vis-www.cs.umass.edu/lfw/
### Paper - https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf
### Youtube Video Reffered for help [Video By Nicolas](https://www.youtube.com/watch?v=bK_k7eebGgc&list=PLgNJO2hghbmhHuhURAGbe6KWpiYZt0AMH&index=1)
## How to Use and Remarks
- I have used used Docker for the hassle free setup of the tensorflow gpu with jupyter notebook
- All the training, preprocessing and inferences are done with this.
### First Create the Following folder structure
```bash
data/
├── anchor/
├── positive/
└── negative/
```
#### and
```bash
application_data/
├── input_image/
└── verification_images/
```
### Fire up your docker container
`docker compose up`
1. > You can run the notebook in that environment but to add the positive and anchor images you have to use `imageCollector,py` file on
> your local machine due to the fact that docker doesn't connect to hardware directly. If you are a linux user you can add
> "/dev/video0:/dev/video0" in the devices in docker compose file. Otherwise its much of a hassle to set it up.
2. > Download the data,unzip it and move the images to desired folders using the notebook.
3. > Go through the notebook for all other steps
#### and for verification it goes the same
> Run `verification.py`
> I have configured it to run on CPU, you can change to GPU as well.