Comments (2)
I think preprocessing is not required for ShanghaiTech since the images are smaller and more uniform. You can run the training step directly
You'll probably have to apply some changes to the proposed dataloadercrowd_sh
I do not agree with you. The min resolution of ShanghaiTech part A <256 and the min resolution of ShanghaiTech part B <512. So you will meet crop size error if you not use preprocess code. And I think the preprocess code is sane as QNRF. But this is a trick to use 512 min resolution "preprocess code" for 256 crop size of ShanghaiTech part A.(Because you enlarge the resolution)
from bayesian-crowd-counting.
I think preprocessing is not required for ShanghaiTech since the images are smaller and more uniform. You can run the training step directly
You'll probably have to apply some changes to the proposed dataloader crowd_sh
from bayesian-crowd-counting.
Related Issues (20)
- Issue about dataloader
- How to pre-process the ShanghaiTech dataset? HOT 3
- The meaning of the density graph? HOT 4
- Where does the variable 'points' define? HOT 1
- Ann .mat file to preprocess the input dataset HOT 2
- the abs in forward HOT 2
- Multi-GPU training HOT 3
- Performance with UCF-QNRF
- Code for NWPU
- When random crop the image, the number of people may be zero. HOT 1
- How to set rights path in linux? HOT 1
- How to add additional data/images to Shanghaitech A and B ?
- What's the meaning of background_radio?
- Why the target defined in the code are not all ones
- Plot density heatmap
- "train.py: error: unrecognized arguments: --data_dir 1 --save_dir" a sample command mistake
- What is the `x` means in the dataloader ? HOT 1
- How do I preprocess JHU++ datasets?
- How to predict my own images? HOT 1
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