https://github.com/ShownX/mxnet-E2FAR

MXNET/Gluon Implementation of End-to-end 3D Face Reconstruction with Deep Neural Networks
https://github.com/ShownX/mxnet-E2FAR

3d reconstruction

Last synced: 2 months ago
JSON representation

MXNET/Gluon Implementation of End-to-end 3D Face Reconstruction with Deep Neural Networks

• Host: GitHub
• URL: https://github.com/ShownX/mxnet-E2FAR
• Owner: ShownX
• License: apache-2.0
• Created: 2018-01-25T00:19:40.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2018-01-25T19:53:55.000Z (almost 7 years ago)
• Last Synced: 2024-08-01T22:40:56.049Z (5 months ago)
• Topics: 3d, reconstruction
• Language: Python
• Size: 10.7 KB
• Stars: 74
• Watchers: 7
• Forks: 15
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• Awesome-MXNet - E2FAR

README

        
# mxnet-E2FAR

This is a MXNet/Gluon Implementation of End-to-end 3D Face Reconstruction with Deep Neural Networks.

1. Download VGG-Face and convert it to the mxnet-weights by running the caffe_converter:

    ```

    python $MXNET/tools/caffe_converter/convert_model.py prototxt weights params_name

    ```

Put the weights into the folder ```ckpt/VGG-Face```

2. Prepare the dataset

3. For train your dataset, you may need to change the ```dataset``` in the main code to fit your dataset

4. Run the code:

    ```

    # fine-tune the branch and fully connected layers

    python E2FAR.py --pretrained --freeze --epoch 10

    # fine-tune whole network

    python E2FAR.py --start_epoch 10

    ```

If you use this code, pls mention this repo and cite the paper:

```

@InProceedings{Dou_2017_CVPR,

author = {Dou, Pengfei and Shah, Shishir K. and Kakadiaris, Ioannis A.},

title = {End-To-End 3D Face Reconstruction With Deep Neural Networks},

booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

month = {July},

year = {2017}

}

```

## Known issues

dataloader is very slow and cannot make fully usage of GPU training.

You can use record io to pack the image and do more augmentation.