updated docs

Author: mese79

docs/assets/Screenshot 2025-09-08 at 13.17.45.png

@@ -9,30 +9,20 @@ You can bring up the *Feature Extractor widget* from the napari **Plugins** menu
 ![plugins menu](assets/plugins_menu.png){width="360"}
 
 ## Widget Tools Description
-![Feature Extractor](assets/extractor_widget/extractor.png){width="360"}
+![Feature Extractor](assets/extractor_widget/extractor_numbered.png){width="360"}
 
 1. **Image Layer**: To select your current image stack.
 2. **Encoder Model**: Sets which model you want to use for feature extraction.  
     The **FF** plugins, by default, comes with `MobileSAM`, `SAM (huge)`, `μSAM_LM (base)`, `μSAM_EM_Organelles (base)`, `DINOv2`, `SAM2 (large)`, and `SAM2 (base)` models. It is also possible to introduce a new model by adding a new [*model adapter*](./model_adapter.md) class.
-3. **Features Storage File**: Where you want to save the features as an `HDF5` file.
-4. **Extract Features** button: Will run the feature extraction process.
-5. **Stop** button: To stop the extraction process!
-
-## CLI Description
-
-You can also extract features by running the `featureforest.feature_extractor` CLI in your terminal.
-
-Here is a brief description of the expected input arguments (see `$ featureforest.feature_extractor -h` for details):
-1. `--input_path`: The filepath to an input image.
-2. `--output_path`: The filepath where the extracted features will be stored for the next step, i.e. [_Segmentation_](./segmentation.md).
-3. `--model_choice`: The choice of model that will be used for feature extraction.
-
-The extraction process might take some time based on number of image slices and the image resolution. This is due to the fact that in **FF** we turn an image into overlapping patches, then pass those patches to the encoder model to get the features. Why we do this? We need to aquire a feature vector per each pixel and not for the whole image. 
+3. **No Patching**: If checked, it means no patching will happen during the feature extraction process. Otherwise, the image will be divided into patches before passing them through the encoder model. By using SAM2 encoders, you might check this box to save some computation power.  
+4. **Features Storage File**: Where you want to save the features as an `HDF5` file.
+5. **Extract Features** button: Will run the feature extraction process.
+6. **Stop** button: To stop the extraction process!
 
 ## Model Selection
 Our experiments tell us usually the `SAM2 (large)` model works the best. However, for less complicated images, using `MobileSAM` or `DINOv2` might also result in a good segmentation as they are lighter and faster.  
 
 !!! note
-    When you use a model for the first time, the model's weight will be downloaded from their repository.  So, you might hit a little delay at the first use of model.  
+    When you use a model for the first time, the model's weight will be downloaded from their repository.  So, you might hit a little delay at the first usage of model.  
 
 Once you have your image features extracted, you can use the [**Segmentation**](./segmentation.md) widget to generate your image masks.

docs/assets/extractor_1.png

@@ -9,13 +9,13 @@ title: Welcome
 ### Overview
 FF plugin includes two widgets: **Feature Extractor** and **Segmentation** widgets.
 
-![Feature Extractor](assets/extractor_1.png){width="300"}
+![Feature Extractor](assets/extractor_widget/extractor_1.png){width="300"}
 /// caption
 Feature Extractor Widget
 ///
 
-![Segmentation](assets/segmentation_1.png){width="300" align=left}
-![Segmentation](assets/segmentation_2.png){width="300" align=right}
+![Segmentation](assets/segmentation_widget/segmentation_1.png){width="300" align=left}
+![Segmentation](assets/segmentation_widget/segmentation_2.png){width="300" align=right}
 /// caption
 Segmentation Widget
 ///