Open anmol19005 opened 4 years ago
Hi @anmol19005
Thanks a lot for your attention on our work. Sorry. We do not provide the cyclegan code. Actually, you can prepare data and train it easily via the official code (https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix).
If you are interested in UNIT-like framework with generation and re-id, you may refer to my previous work DG-Net https://github.com/NVlabs/DG-Net
Hi Zhedong,
I wanted to know about the distribution of validation (query) and gallery dataset (like class distribution), used for reranking. Also have you used Image cloning? If yes can you give me a brief about your implementation?
Thank you, Anmol Jain AI, Postgrad
On Sat, Oct 17, 2020 at 4:25 AM Zhedong Zheng notifications@github.com wrote:
Hi @anmol19005 https://github.com/anmol19005
Thanks a lot for your attention on our work. Sorry. We do not provide the cyclegan code. Actually, you can prepare data and train it easily via the official code ( https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix).
If you are interested in UNIT-like framework with generation and re-id, you may refer to my previous work DG-Net https://github.com/NVlabs/DG-Net
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Hi Zhedong,
I wanted to ask how do you train the Cycle-GAN. Do you use the synthetic and real data given by the challenge? However, you need real-synthetic vehicle pairs, which we don't have, right?
Thank you, Marta Fernández,
Hi @Martafdez18
Cycle-GAN do not require pairs, but two domain sets. In fact, you only need two folders to train cycle-gan for style changing.
just go to cycle-gan repo and follow the instructions.
On Fri, Feb 12, 2021 at 4:46 PM Zhedong Zheng notifications@github.com wrote:
Hi @Martafdez18 https://github.com/Martafdez18
Cycle-GAN do not require pairs, but two domain sets. In fact, you only need two folders to train cycle-gan for style changing.
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I was going through your research paper and was parallelly going through the codes. I was not able to find how you used UNIT(cycle gan) to perform style transfer and then content manipulation. Kindly help us understand the flow of your code or provide a link where you have trained synthetic images on cycleGAN.