Comments (7)
@qubvel Nice, Please update the README.md. and I want to ask a question, When I ues resnet34 as backbone, It seems the .weights can not be download, the download url has been deleted?
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@qubvel The problem has been solved?
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Not yet, will try to fix it today
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@Gary-Deeplearning check just now 'resnet34' backbone for Unet model, weights have been downloaded and it works fine with keras 2.1.5. May be the problem in keras version, will check it too.
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@qubvel yeah, It works. But when I install the segmentation_models in the GPU server, it got a strange problem: ```
Collecting segmentation_models
Could not find a version that satisfies the requirement segmentation_models (from versions: )
No matching distribution found for segmentation_models
You are using pip version 9.0.1, however version 18.0 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
But I can also use this to solve the install.
$ git clone https://github.com/qubvel/segmentation_models.git
$ cd segmentation_models
$ git submodule update --init --recursive
May be it was the problem of clound.
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@Gary-Deeplearning I suppose you use python version less than 3.6
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@qubvel yeah, It's python version problem. Thanks for explaining.
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Related Issues (20)
- Dropout layers in U-net and Linknet
- ModuleNotFoundError: No module named 'keras.legacy_tf_layers' issue
- Kindly add the Segformer Backbone! :) HOT 1
- module 'keras.utils.generic_utils' has no attribute 'get_custom_objects' HOT 4
- Good IOU Score on training data, but bad segmentation on testing data. HOT 2
- batch size when predicting HOT 1
- Segformer/Transformer Backbone
- File "/usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/interpreter.py", line 915, in invoke self._interpreter.Invoke() RuntimeError: tensorflow/lite/kernels/concatenation.cc:158 t->dims->data[d] != t0->dims->data[d] (1 != 2)Node number 304 (CONCATENATION) failed to prepare.
- Is there option to add classification head after encoder like in pytorch version?
- AttributeError: module 'keras.utils' has no attribute 'generic_utils'
- Incorporating sample weights in loss function
- Understanding difference between TensorFlow and PyTorch implementations of Unet
- Equation .. math:: is misleading
- How to apply inferred mask to image
- massive datasets loading and training
- Impact of pre-trained weights ? HOT 2
- train Unet model with gray scale
- Can I use my own custom feature extraction model to export to the backbone? HOT 3
- Model segment
- AttributeError: module 'keras.utils' has no attribute 'generic_utils' HOT 1
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