From 1b5881de3cc7ad3955f176c732a874aca7fab63f Mon Sep 17 00:00:00 2001 From: Giovanni Volpe Date: Thu, 5 Dec 2024 07:26:50 +0100 Subject: [PATCH] Update README.md --- README.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index 4ad78ed10..12d3e375c 100644 --- a/README.md +++ b/README.md @@ -52,12 +52,12 @@ DeepTrack is a general purpose deep learning framework for microscopy, meaning y
- 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


@@ -69,9 +69,9 @@ DeepTrack is a general purpose deep learning framework for microscopy, meaning y
- 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


@@ -93,19 +93,19 @@ 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_cnn_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. 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_cnn_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. 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. +3. Demonstrating how to customize models using layer-blocks. ## DeepTrack 2.1 in action