Comments (2)
Hi, @MahlerMozart
FCN-32s uses VGG-16 under the hood. In this case, the resnet is used.
Due to differences in models (more specifically, usage of Batch normalization
layers in resnet), we use different learning rate, batch size, crop size. This was done
more or less empirically.
Let me know if it is clear enough.
from pytorch-segmentation-detection.
Hi Daniil,
That makes a lot of sense. I will try to train your model on my dataset to see how it works. Thanks.
from pytorch-segmentation-detection.
Related Issues (20)
- about Resnet18_8s HOT 2
- Unable to run resnet_34_8s_demo.ipynb HOT 8
- CRF implementation HOT 1
- Could you please add a license file HOT 2
- Incompatible ResNet arguments HOT 1
- new model implementation
- Difference Between Semantic Segmentation and Image Classification
- I want to train on my own custom dataset.What is the process of doing that?What should be the format of annotations?
- Error when trying to run resnet_34_8s_test HOT 1
- RuntimeError: value cannot be converted to type float without overflow HOT 1
- RuntimeError: Error(s) in loading_state_dict for VGG
- Error(s) in loading state_dict for Resnet18_8s HOT 7
- Optimizer for unet model on Pascal Voc segmentation
- FCN Skip Connections HOT 1
- fully_conv in vgg16 HOT 1
- No such file or directory: 'resnet_34_8s_66.pth' HOT 6
- Train on my own dataset without superpixels HOT 4
- adaptive_computation_time HOT 2
- Changing the number of classes HOT 7
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from pytorch-segmentation-detection.