Differences

This shows you the differences between two versions of the page.

--- eman2:e2tomo_more [2025/08/05 01:05] – [Automated particle selection] steveludtke
+++ eman2:e2tomo_more [2025/08/05 02:17] (current) – steveludtke
@@ Line 39: / Line 39: @@
 . Go through all tomograms in the list and apply the network to select the particles. These particles can be viewed and modified in //e2spt_boxer.py//, and extracted through the particle extraction steps of the main workflow.
- {{http://blake.bcm.edu/dl/EMAN2/sptboxer_convnet.png| Automated particle selection |width=600}}
+ {{https://blake.bcm.edu/dl/EMAN2/sptboxer_convnet.png}}
 Description of items on the GUI:
   * **New/Save/Load**: Initialize a new CNN / save the current trained network to disk / load a trained network from disk.
-  * **!ChangeBx** : Change the box size of positive/negative samples. Ideally, the particles should be recognizable visually from the reference images. The process can be slow if the references come from multiple tomograms.
+  * **ChangeBx** : Change the box size of positive/negative samples. Ideally, the particles should be recognizable visually from the reference images. The process can be slow if the references come from multiple tomograms.
   * **Reference/Particle** selection box: Display circles of references or particles in the tomogram slice view.
-  * **!TargetSize** : This controls the size of target area used for CNN training. i.e. particles should be centered in this region in positive samples, and there should not be particle features in this region in negative samples. The region is defined as a Gaussian function and value here is the sigma of the Gaussian.
+  * **TargetSize** : This controls the size of target area used for CNN training. i.e. particles should be centered in this region in positive samples, and there should not be particle features in this region in negative samples. The region is defined as a Gaussian function and value here is the sigma of the Gaussian.
   * **Learnrate** : Learning rate for the CNN training. Normally no need to change this...
-  * **!PtclThresh** : The intensity threshold in the neural network output to be recognized as a particle. The target of positive samples should be 1 and negative samples should be 0.
+  * **PtclThresh** : The intensity threshold in the neural network output to be recognized as a particle. The target of positive samples should be 1 and negative samples should be 0.
-  * **!CircleSize** : The radius of circles in pixels on the tomogram slice view. This also controls the closest distance between particles.
+  * **CircleSize** : The radius of circles in pixels on the tomogram slice view. This also controls the closest distance between particles.
   * **Sum/Max** selection box : Choose between different modes of the loss function. **Sum** is used for globular particles that are generally confined in the target area. In **Max** mode, the CNN only assume there are particle features that exist within the region. It is harder to train than the **Sum** mode, but allows particles of irregular shapes, such as protein fibers.