From 060d664896bd3dae3c7244c39973571a5c91b2de Mon Sep 17 00:00:00 2001 From: Alex <95913221+Pwhsky@users.noreply.github.com> Date: Fri, 13 Dec 2024 12:12:15 +0100 Subject: [PATCH] Update 1.7 readme --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 4ad78ed10..8908cb7dd 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@

PyPI version - PyPI version + PyPI version Python version @@ -14,8 +14,8 @@

Installation • - Examples • - Basics • + Examples • + BasicsCite usLicense

@@ -95,7 +95,7 @@ We have two separate series of notebooks which aims to teach you all you need to 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. +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. @@ -123,10 +123,10 @@ Additionally, we have seven more case studies which are less documented, but giv We also have examples that are specific for certain models. This includes - [*LodeSTAR*](examples/LodeSTAR) for label-free particle tracking. -- [*MAGIK*](deeptrack/models/gnns/) for graph-based particle linking and trace characterization. +- [*MAGIK*](examples/MAGIK) for graph-based particle linking and trace characterization. ## Documentation -The detailed documentation of DeepTrack 2.1 is available at the following link: https://softmatterlab.github.io/DeepTrack2/deeptrack.html +The detailed documentation of DeepTrack 2.1 is available at the following link: https://deeptrackai.github.io/DeepTrack2 ## Video Tutorials