+ 
-
+
Training a CNN-based single particle tracker using simulated data
+ Training a CNN-based single particle tracker using simulated data
- Unsupervised training of a single particle tracker using LodeSTAR
+ Unsupervised training of a single particle tracker using LodeSTAR
- Training LodeSTAR to detect multiple cells from a single image
+ Training LodeSTAR to detect multiple cells from a single image
Training a UNet-based multi-particle tracker using simulated data
+ Training a UNet-based multi-particle tracker using simulated data
- Training MAGIK to trace migrating cells
+ Training MAGIK to trace migrating cells
# Basics to learn DeepTrack 2.1
@@ -93,19 +93,15 @@ Everybody learns in different ways! Depending on your preferences, and what you
We have two separate series of notebooks which aims to teach you all you need to know to use DeepTrack to its fullest. The first is a set of six notebooks with a focus on the application.
-1. deeptrack_introduction_tutorial gives an overview of how to use DeepTrack 2.1.
-2. tracking_particle_cnn_tutorial demonstrates how to track a point particle with a convolutional neural network (CNN).
-3. tracking_particle_unet_tutorial demonstrates how to track multiple particles using a U-net.
-4. characterizing_aberrations_tutorial demonstrates how to add and characterize aberrations of an optical device.
-5. distinguishing_particles_in_brightfield_tutorial demonstrates how to use a U-net to track and distinguish particles of different sizes in brightfield microscopy.
-6. analyzing_video_tutorial demonstrates how to create videos and how to train a neural network to analyze them.
+1. tracking_particle_cnn_tutorial demonstrates how to track a point particle with a convolutional neural network (CNN).
+2. tracking_particle_unet_tutorial demonstrates how to track multiple particles using a U-net.
+3. distinguishing_particles_in_brightfield_tutorial demonstrates how to use a U-net to track and distinguish particles of different sizes in brightfield microscopy.
The second series focuses on individual topics, introducing them in a natural order.
-1. Introducing how to create simulation pipelines and train models.
-2. Demonstrating data generators.
-3. Demonstrating how to customize models using layer-blocks.
+1. Introducing how to create simulation pipelines and train models.
+2. Demonstrating data generators.
## DeepTrack 2.1 in action