Comments (3)
@siyazingca Does the image contain one attribute (e.g., one-hot vector label)? Then, you can train the model on your dataset in the same way as training it on RaFD.
$ python main.py --mode='train' --dataset='RaFD' --c_dim='dimension of one-hot vector' --image_size='image resolution you want' \
--num_epochs='number of epochs' --num_epochs_decay='number of epochs to decay' --sample_step='number of iteration for sampling' --model_save_step='number of iteration for saving model checkpoints' \
--sample_path='stargan_your_dataset/samples' --log_path='stargan_your_dataset/logs' \
--model_save_path='stargan_your_dataset/models' --result_path='stargan_your_dataset/results'
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@yunjey thanks for you sharing your work and this is a good work, can you provide more information about how to prepare the one-hot vector label on RaFD?
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@happsky You don't need to provide the one-hot vector label manually. Only you need to do is to create your data folder structure as described here. Then you can use the command below.
$ python main.py --mode='train' --dataset='RaFD' --c_dim=8 --image_size=128 \
--num_epochs=200 --num_epochs_decay=100 --sample_step=200 --model_save_step=200 \
--sample_path='stargan_rafd/samples' --log_path='stargan_rafd/logs' \
--model_save_path='stargan_rafd/models' --result_path='stargan_rafd/results'
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Related Issues (20)
- request for RaFD dataset
- change IMAGE_SIZE
- How to align Rafd dataset?
- Evaluation Metric "Classification Error" on RaFD dataset?
- Error when testing
- Got stuck at self.G.to(self.device) HOT 3
- Not using Epoch but Iteration to train HOT 1
- Manage domain (attribute) as StarGANv1 HOT 1
- Missing Classification loss in Discriminator for fake Images HOT 2
- Question about parameter adjustment when changing attributes
- Can you share your pre-trained model on RaFD dataset? HOT 1
- [Question] Mask vector in this paper
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- Hello, can you provide the multi-attribute translation task code of solver.py?
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- Question: Instance Normalization with track_running_stats= True
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