Variations-of-SFANet-for-Crowd-Counting
Variations-of-SFANet-for-Crowd-Counting copied to clipboard
Pongpisit-Thanasutives
Train detail?
I have trained 1000 epoche in shanghaiTech part A samples using the M_SFANet，but the test mea is 68.08，the learning rate used 5e-4, the batch size used 8, and the sample size croped as 400*400. And can you tell me the train detail?
Do you use the training preprocessing (for SHA) as employed in the Training details section of SFANet paper (https://arxiv.org/pdf/1902.01115.pdf). And also, in my experiments, I used ADAM with lookahead (https://arxiv.org/abs/1907.08610, See https://github.com/rwightman/pytorch-image-models/blob/master/timm/optim/lookahead.py for the implementation). You can also play with the learning rate (5e-4, 6e-4, ...).
how could you set crop size to 400x400
when i process the shha data according to the bayesian code, erroe happen like this : File "/home/../.jupyter/Variations-of-SFANet-for-Crowd-Counting-master/datasets/crowd.py", line 91, in train_transform │·················
assert st_size >= self.c_size │·················
AssertionError
only when i set the crop size to 256( other setiings like yours ),the code could run,but the test mae is only 73.6, far from the paper record.
and i was strcucked about the min_size and max_size in the bayesian_preprocess_sh.py(shha setting is min_size = 256, max_size = 5096
I have trained 1000 epoche in shanghaiTech part A samples using the M_SFANet，but the test mea is 68.08，the learning rate used 5e-4, the batch size used 8, and the sample size croped as 400*400. And can you tell me the train detail?
how could you set crop size to 400x400 when i process the shha data according to the bayesian code, erroe happen like this : File "/home/../.jupyter/Variations-of-SFANet-for-Crowd-Counting-master/datasets/crowd.py", line 91, in train_transform │················· assert st_size >= self.c_size │················· AssertionError only when i set the crop size to 256( other setiings like yours ),the code could run,but the test mae is only 73.6, far from the paper record. and i was strcucked about the min_size and max_size in the bayesian_preprocess_sh.py(shha setting is min_size = 256, max_size = 5096
Just to clarify some points, (1) The purpose of "bayesian_preprocess_sh.py" is for fine-tuning the models which are pretrained on UCF_QNRF. So, the crop size should be 256x256 rather than 400x400 if you are using (fine-tuning) "bayesian_preprocess_sh.py" according to their paper (https://openaccess.thecvf.com/content_ICCV_2019/papers/Ma_Bayesian_Loss_for_Crowd_Count_Estimation_With_Point_Supervision_ICCV_2019_paper.pdf).
(2) If you are training from scratch, the crop size is 400x400 and please refer to the SHA preprocessing code here => https://github.com/pxq0312/SFANet-crowd-counting/blob/master/transforms.py.
3qu for clarification, another problem is how to get the average prediction of two models(M-segNet and M-sfanet）,just to train the two model on the same dataset and average the prediction to get the final attracting results?
3qu for clarification, another problem is how to get the average prediction of two models(M-segNet and M-sfanet）,just to train the two model on the same dataset and average the prediction to get the final attracting results?
Yes!, We used simple averaging model predictions.