Comments (6)
@ljjcoder This is the right script to test FID. However, since it's using Inception model, make sure that you run the script on GPU using --gpu
flag:
python ./scripts/fid_score.py --path [path_to_ground_truth] [path_to_output] --gpu 1
The warning message you are receiving is because the pre-trained Inception model is trained using PyTorch < 1.0.0 and can be ignored for now!
Measuring FID is a very CPU intensive process and it requires a lot of RAM, and you normally need 10,000+ images to get an acceptable result! In our experiments, on a Titan V GPU and an Intel Xeon with 8 cores, it takes more than 2 minutes to calculate and it eats up almost 25G of RAM.
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@knazeri ,thanks for your reply!when test the fid on places2 ,the score is reasonable(the better result from a human point of view will get lower fid scores).But when test it on celebA, the score seems to not depict the quality of the generated image(the better result from a human point of view will get higher fid scores). Maybe the the inception model trained on imagenet is not suitalbe for test faces result?
from edge-connect.
@ljjcoder The inception model is only used to extract deep features from input images. The Frechet distance measures the distance between two multivariate normals, that means the input distribution has to be diverse (large) enough to be considered normal! We used 10,000 images to evaluate FID on CelebA and Figure 13 in our paper shows the efficacy of FID!
How many images did you test with? What are the FID values you are receiving?
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@knazeri , On celebA, the mask hole is 128x128 square, and the image is 256x256, I test 1000 images, I get fid score is 14.24 use CA model. but I use another model to produce the inpainted results which are better than CA, geting higher result 14.86. there are some CA result:
there are some another model result:
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@ljjcoder First off, 1,000 images are not enough to capture an entire distribution. I've seen in papers that the FID is reported for more than 10,000 (sometimes 25,000) images!
Second, please note that FID takes an entire distribution into account and measures the distance between the mean and covariance of two distributions. That means even though some images might not be visually pleasing, the overall quality (of the entire set) might be good!
Having said that, none of these quantitative measures (FID included) are perfect! Still, the human study remains the best qualitative measure to evaluate generative models!
from edge-connect.
Hello, when calculating FID, do you compare 10,000 result pictures with 10,000 GT pictures, or compare 10,000 result pictures with all GT pictures?
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Related Issues (20)
- Test image is being filled in a lighter shade HOT 1
- Who can help me slove this error? (when I try to train ) HOT 5
- Run the program on CoLab
- Convergency of edge model HOT 10
- Hello, After reading your paper, may I have a question that why you choice 178 for the celebA dataset drop size.
- 如果对图像修复,edge-connect感兴趣,或者需要帮助,可以联系我
- Training on Google Colab immediately stops HOT 1
- Selection of dataset
- Canny sigma HOT 1
- how to implement the visualization for the learned edges? HOT 2
- Sizes of tensors must match except in dimension 1
- New easy to use inpanting method with transformers HOT 1
- When using edge=2, training has ValueError: operands could not be broadcast together with shapes (256,256,3) (256,256)
- Why is there an error when I train MODEL4: joint model/为什么我训练MODEL4 :joint model会报错
- When I tried to start training, I got an error:RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [1, 512, 4, 4]] is at version 2; expected version 1 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True). HOT 15
- About precision and recall during training HOT 1
- The loss function is abnormal when the edge network is trained
- RuntimeError: CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
- a question
- Edge Model Not converging
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