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About the training of CUB200 dataset #41
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我也遇到这个问题了, 我在CUB上 用VQ-diffusion-B训练的图像很抽象,但是用CelebA-HQ训练的人脸还可以,不知道CUB数据集怎么训练的?请问你这个效果是怎么训练出来的? |
我这个其实算是VQ-diffusion-F,是用了pretrained model的大约300epoch的结果,但是测试结果的fid比论文高了10个点。我也跑过VQ-diffusion-B,结果就更差了。我个人怀疑是CUB数据量太小,导致直接训练的结果很差。但是如果用了pretrained model的情况下结果还是这么差的情况就很令人费解了。我打算在coco上再尝试一下,看看数据量的影响有多大。 |
请问这个文件在哪里OUTPUT/pretrained_model/taming_dvae/taming_f8_8192_openimages_last.pth,我找半天没找到,谢谢! |
请问你有这个文件吗OUTPUT/pretrained_model/taming_dvae/taming_f8_8192_openimages_last.pth,我没找到在哪,谢谢! |
@pangPhD 你需要从他给出的pretrain.sh下载 if [ -f taming_f8_8192_openimages_last.pth ]; then if [ -f vqgan_imagenet_f16_16384.pth ]; then if [ -f ViT-B-32.pt ]; then |
Regarding the training of cub200, I followed all the parameter settings of the source code. I first loaded cc_learned.pth and started training. According to the paper, this should be the VQ-diffusion-F model. After training up to 300epoch, I observed that the val loss has almost stabilized and tested it on 299epoch. But the test result is very bad with a fid of 28. I am curious why this is the case.
My test command is:
VQ_Diffusion_model.inference_generate_sample_with_condition(data,truncation_rate=1.0, save_root="pre/ep299_tr1_gs5",batch_size=1, guidance_scale=5.0)
Here is the tensorboard and the visualization of test result:
I also tried to train VQ-diffusion-B which has no pretrained model. But the result is worse.
Does anyone encounter the same problem?
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