Comments (1)
Hi @Tahedi1
Look at this file. Just replace get_backbone
by your own backbone and provide layer names/numbers to get features from.
from segmentation_models.
Related Issues (20)
- 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 HOT 1
- 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
- Training error
- RecursionError: maximum recursion depth exceeded in comparison
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from segmentation_models.