Comments (2)
classes
config is the model-specific config. It decides the number of final output neurons of the fully-connected layer.
As cell segmentation is binary problem (0:bgd, 1:cell), we either use 1 or 2 neurons. We used a single neuron for the segmentation.
In the config file, you can see we set 3 for the classes
. Here, the other two dimensions each stand for x-axis and y-axis gradient field. They predicts the relative position w.r.t. center of the cell center (median of h,w).
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Although we also mentioned in our paper as "Gradient Tracking" Sections, the act of how this tracking works is explained in more details at the CellPose paper. The gradient tracking parts in our code is a refactored version of their official code.
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Related Issues (19)
- Running on large jp2 WSI files HOT 6
- ERROR: Could not find a version that satisfies the requirement MEDIAR HOT 1
- KeyError: 'medair'
- RuntimeError: Found no NVIDIA driver on your system. HOT 1
- ModuleNotFoundError: No module named 'train_tools' HOT 2
- Running the predict.py code does not produce segmentation results. HOT 1
- requirements.txt has package version "0.0" for skimage HOT 1
- Please retain the Cellpose copyright as required by the BSD-3 license HOT 7
- Finetuing the "finetuned" model on custom dataset HOT 2
- current Mediar weights HOT 2
- Access to data used for inference HOT 1
- Train on custom dataset HOT 2
- Parameter name mismatches and other issues HOT 2
- Public dataset preprocessing and public data selection strategy for pretraining HOT 4
- Poor Performance - is my input correctly formated?
- MEDIAR package HOT 1
- Fine-tuning issues HOT 10
- knn classifier HOT 1
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