Comments (2)
- yes, resnet models trained with images in BGR
- no, images should be in range 0-255 (models have batchnorm as the first layer)
- it is not very important to freeze, you can start train all model layers from the first epoch.
from segmentation_models.
I obtain better results when I provide normalized input data, strange..
from segmentation_models.
Related Issues (20)
- module 'segmentation_models.losses' has no attribute 'DiceLoss' HOT 1
- Call arguments received by layer 'model_18' (type Functional): • inputs=tf.Tensor(shape=(1, 384, 8), dtype=float32) • training=True • mask=None
- Any way to get per class IoU scores?
- How to get PA value and mPA value? HOT 1
- How to extract saliency map from the ImageNet pretrained with non-RBG input?
- How to implement more metrics in model.compile? HOT 2
- 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
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from segmentation_models.