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Inference problem
After I train the network with multiple classes(11), the inference result is confusing. In applyOnImage.py: The first problem is in the line: imsave(save_path, ut.combine_image_blobs(image_raw, pred_blobs)): ValueError: Invalid shape for image array: (10, w,h, c); the counts[None]: [[7. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
so I add pred_blobs_max = np.argmax(pred_blobs,axis=0), imsave(save_path, ut.combine_image_blobs(image_raw, pred_blobs_max)) and it works
However, here the second problem comes: the visualization result of prediction images is very confusing, it seems that the count is correct but the pixel location is not on the object
the dataset is the same to pascal.py
Thank you very much! @IssamLaradji
hmmm strange. Is your dataset publicly available? I can test it on my end. Thanks.
hmmm strange. Is your dataset publicly available? I can test it on my end. Thanks.
Ok, my dataset is NWPU VHR-10, the link is: https://1drv.ms/u/s!AmgKYzARBl5cczaUNysmiFRH4eE from page http://www.escience.cn/people/gongcheng/NWPU-VHR-10.html Thanks, I wonder know whether my dataset.py is right
Hi @tongpinmo the code has been refactored and should work better now. Did you try it with this new version of the code? Thanks.