Comments (3)
Hi @prashant-bansod ,
You may find this Jupyter Notebook helpful.
You need to add extra parameters to the load_model
function as follows:
from instance_normalization import InstanceNormalization
from my_upsampling_2d import MyUpSampling2D
from FgSegNet_v2_module import loss, acc, loss2, acc2
model = load_model(model_path, custom_objects={'MyUpSampling2D': MyUpSampling2D, 'InstanceNormalization': InstanceNormalization, 'loss':loss, 'acc':acc, 'loss2':loss2, 'acc2':acc2})
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@lim-anggun Thank you very much. I made the required corrections. Thanks a ton for your reply.
I had one more question, I tried the pedestrian model but the silhouettes I got are noisy. Does the background has an effect on the extracted silhouettes?
What do you think would be the best approach to extract human silhouettes?
from fgsegnet_v2.
thanks a lot for your great work, but how to make my own data like the CDnet2014? and how to train it?
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Related Issues (20)
- multispectral use HOT 1
- how to train my own data? HOT 1
- How to handle real video HOT 3
- Evaluation Code HOT 1
- Can it work well on other dataset?
- the accuracy of a catogery is very low the port_0_17fps : 0.435026037734113 HOT 1
- Memory leak and how to train using a gpu ? HOT 3
- compilation error HOT 1
- Training on real video
- How can I load model? HOT 5
- ImportError: libcublas.so.8.0: cannot open shared object file: No such file or directory
- How can i solve this error? HOT 1
- How can I train with my own video sequence? HOT 3
- About the meaning of void_label HOT 1
- License File HOT 1
- foreground segment images HOT 1
- Design of decoder HOT 1
- CDnet Utilities link address is missing HOT 1
- how can I evaluate the segmentation results quantificationally? HOT 3
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