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2022-04-25 11:52:47 +08:00
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@@ -70,16 +70,22 @@ In order to ensure the normal training, the batch size must be greater than 1.
Friendly reminder, due to the difference in training settings, the user-trained model will have subtle differences in visual effects from the pre-trained model we provide.
- Train 224 models with VGGFace2 224*224 [[Google Driver] VGGFace2-224 (10.8G)](https://drive.google.com/file/d/19pWvdEHS-CEG6tW3PdxdtZ5QEymVjImc/view?usp=sharing) [[Baidu Driver] ](https://pan.baidu.com/s/1OiwLJHVBSYB4AY2vEcfN0A) [Password: lrod]
For faster convergence and better results, a large batch size (more than 16) is recommended!
***We recommend training more than 400K iterations (batch size is 16), 600K~800K will be better, more iterations will not be recommended.***
```
python train.py --name simswap224_test --batchSize 4 --gpu_ids 0 --dataset /path/to/VGGFace2HQ --Gdeep False
python train.py --name simswap224_test --batchSize 8 --gpu_ids 0 --dataset /path/to/VGGFace2HQ --Gdeep False
```
[Colab demo for training 224 model](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/train.ipynb)
For faster convergence and better results, a large batch size (more than 16) is recommended!
- Train 512 models with VGGFace2-HQ 512*512 [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ).
```
python train.py --name simswap512_test --gpu_ids 0 --dataset /path/to/VGGFace2HQ --Gdeep True
python train.py --name simswap512_test --batchSize 16 --gpu_ids 0 --dataset /path/to/VGGFace2HQ --Gdeep True
```